Search Results (21)

The retrieval of radial ocean surface current from Synthetic Aperture Radar (SAR) data is important for ocean current research and effective ocean remote sensing. Existing algorithms, primarily based on the Average Cross-Correlation Coefficient (ACCC) method, suffer from drawbacks, including low Doppler frequency-shift estimation accuracy and susceptibility to azimuth ambiguity, hindering accurate measurements. To address these limitations, this paper proposes a method for high-resolution radial current velocity estimation. This approach employs Maximum A Posteriori (MAP) estimation based on signal modeling of the local Doppler power spectrum. This method achieves better Doppler frequency shift estimation accuracy than ACCC and effectively mitigates the azimuth ambiguity, substantially enhancing the precision of radial ocean surface velocity estimation. The algorithm was validated using raw Sentinel-1 Strip-map mode real data and HYCOM data acquired over the Seychelles Islands on 23 April 2023, and the central Indian Ocean (south of the equator) on 20 May 2023. Compared with the Sentinel-1 Level 2 ocean Surface Radial Velocity (RVL) product, the method demonstrates the improvements in both spatial resolution and retrieval accuracy. Specifically, the quantitative comparison with HYCOM data showed a reduction in Root Mean Square Error (RMSE) of up to 34.3% and an improvement in Mean Absolute Error (MAE) of up to 32.1%. Moreover, its ability to suppress the azimuth Doppler ambiguity is demonstrated in the real-data experiment. Full article

The ODYSEA (Ocean DYnamics and Surface Exchange with the Atmosphere) mission will provide simultaneous two-dimensional measurements of currents and winds for the first time. According to the ODYSEA radar concept, with a high incidence angle, current noise is primarily driven by backscattered power, which is triggered by wind speed. Therefore, random noise will affect the quality of observations. In low wind conditions, the absence of surface roughness increases the noise level considerably, to the point where the measurement becomes unusable, as the error can exceed 3 m/s at 5 km posting compared to mean current amplitudes of tens of cm/s. Winds higher than 7.5 m/s enable current measurements at 5 km posting with an RMS accuracy below 50 cm/s, but derivatives of currents will amplify noise, hampering the understanding of ocean dynamics and the interaction between the ocean and the atmosphere. In this context, this study shows the advantages and limitations of using noise-reduction algorithms. A convolutional neural network, a UNet inspired by the work of the SWOT (Surface Water and Ocean Topography) mission, is trained and tested on simulated radial velocities that are representative of the global ocean. The results are compared with those of classical smoothing: an Adaptive Gaussian Smoother whose filtering transfer function is optimized based on local wind speed (e.g., more smoothing in regions of low wind). The UNet outperforms the kernel smoother everywhere with our simulated dataset, especially in low wind conditions (SNR << 1) where the smoother essentially removes all velocities whereas the UNet mitigates random noise while preserving most of the signal of interest. Error is reduced by a factor of 30 and structures down to 30 km are reconstructed accurately. The UNet also enables the reconstruction of the main eddies and fronts in the relative vorticity field. It shows good robustness and stability in new scenarios. Full article

Doppler scatterometers have demonstrated the ability to measure wide-swath ocean surface currents from airborne platforms. Since platform velocities for spaceborne platforms are almost two orders of magnitude larger, errors in the knowledge of the pointing of the radar antenna result in ocean current errors that are also two orders of magnitude larger, and this presents a major challenge to achieving useful measurements of ocean currents. Here, we present a new calibration method to estimate pointing biases that removes the dominant pointing errors, allowing for the retrieval of global ocean currents with modest requirements for system stability. The method uses the fact that pointing errors have a velocity signature that depends on cross-track distance (or azimuth angle) alone, while ocean currents do not, if averaged sufficiently along-track. This lack of correlation between error and true currents allows the use of along-track averages of residual radial velocity, after possibly subtracting prior estimates of the currents, for the inversion of the slowly varying pointing errors. The calibration method can be implemented in ground processing and does not impact the processing of onboard data. We illustrate the performance of the calibration on the performance of the proposed NASA/CNES ODYSEA Doppler scatterometer and assess its ability to meet the mission science goals. Full article

Ocean surface radial current velocities can be derived from synthetic aperture radar (SAR) Doppler shift observations using the Doppler centroid technique and a recently developed Doppler velocity model. However, comprehensive evaluations of the accuracy and reliability of these retrievals remain limited. To address this gap, we analyzed 6341 Sentinel-1 SAR scenes acquired over the South China Sea (SCS) between December 2017 and October 2023, in conjunction with drifting buoy observations, to systematically validate the retrieved radial current velocities. A linear fitting method and the dual co-polarization Doppler velocity (DPDop) model were applied to correct for the influence of non-geophysical factors and sea state effects. The validation against the drifter data yielded a bias of 0.01 m/s, a root mean square error (RMSE) of 0.18 m/s, and a mean absolute error (MAE) of 0.16 m/s. Further comparisons with the Surface and Merged Ocean Currents (SMOC) dataset revealed bias, RMSE, and MAE values of 0.07 m/s, 0.14 m/s, and 0.12 m/s in the Beibu Gulf, and −0.06 m/s, 0.23 m/s, and 0.19 m/s in the Kuroshio intrusion area. These results demonstrate that SAR Doppler measurements have a strong potential to complement existing ocean observations in the SCS by providing high-resolution (1 km) ocean surface current maps. Full article

High-frequency (HF) ocean radars have become essential tools for monitoring surface currents, offering real-time, wide-area coverage with cost-effectiveness. This study compares the compact CODAR system (MABT, 13 MHz) and the square-array phased-array radar (KNTN, 8 MHz) deployed at Cape Maobitou, Taiwan. Radial velocity measurements were evaluated against data from the Global Drifter Program (GDP), and a quality control (QC) mechanism was applied to improve the data’s reliability. The results indicated that KNTN provides broader spatial coverage, whereas MABT demonstrates higher precision in radial velocity measurements. Baseline velocity comparisons between MABT and KNTN revealed a correlation coefficient of 0.77 and a root-mean-square deviation (RMSD) of 0.23 m/s, which are consistent with typical values reported in previous radar performance evaluations. Drifter-based velocity comparisons showed an initial correlation of 0.49, with an RMSD of 0.43 m/s. In more stable oceanic regions, the correlation improved to 0.81, with the RMSD decreasing to 0.24 m/s. To clarify, this study does not include multiple environmental scenarios but focuses on cases where both radar systems operated simultaneously and where surface drifter data were available within the overlapping area. Comparisons are thus limited by these spatiotemporal conditions. Radar data may still be affected by environmental or human factors, such as ionospheric variations, interference from radio frequency management issues, or inappropriate parameter settings, which could reduce the accuracy and consistency of the observations. International ocean observing programs have developed quality management procedures to enhance data reliability. In Taiwan, the Taiwan Ocean Research Institute (TORI) has established a data quality management mechanism based on international standards for data filtering, noise reduction, and outlier detection, improving the accuracy and stability of radar-derived velocity measurements.To eliminate the effects caused by different center frequencies between MABT and KNTN, this study used the same algorithms and parameter settings as much as possible in all steps, from Doppler spectra processing to radial velocity calculation, ensuring the comparability of the data. This study highlights the strengths and limitations of compact and phased-array HF radar systems based on co-observed cases under consistent operational conditions. Future research should explore multi-frequency radar integration to enhance spatial coverage and measurement precision, improving real-time coastal current monitoring and operational forecasting. Full article

In this work, we present the results of a comparative analysis between the first-generation Advanced Synthetic Aperture Radar (ASAR) sensor mounted on board the ENVISAT platform and the novel ICEYE micro-satellite synthetic aperture radar (SAR) sensor in measuring the radial velocity of ocean currents through the Doppler Centroid Anomaly (DCA) technique. First, the basic principles of DCA and the theoretical precision of the Doppler Centroid (DC) estimates are introduced. Subsequently, the role of the DC measurements in retrieving the sea surface current velocity is addressed. To achieve this goal, two sets of SAR data gathered by ASAR (C-band) and from the X-band ICEYE instruments, respectively, are exploited. The standard deviation of DCA measurements is derived and tested against what is expected by theory. The presented analysis results are beneficial to evaluate the pros and cons of the new-generation X-band to the first-generation ASAR/ENVISAT system, which has been extensively exploited for ocean currents monitoring applications. As an outcome, we find that with inherently selected methods for DC estimates, the performance offered by ICEYE is comparable to, or even better than (with specific parameters selection), the consolidated approaches based on the ASAR sensor. Nonetheless, new SAR constellations offer an undoubted advantage regarding improved spatial resolution and time repeatability. Full article

Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand these important dynamic processes by measuring two-dimensional fields of total surface current and wind vectors with unparalleled spatial and temporal resolution (1 × 1 km² or finer, 1 day) and unmatched precision over one continuous wide swath (100 km or more). This paper presents a comprehensive numerical analysis of the expected performance of the Earth Explorer 11 (EE11) SeaSTAR mission candidate in the case of idealised and realistic 2D ocean currents and wind fields. A Bayesian framework derived from satellite scatterometry is adapted and applied to SeaSTAR’s bespoke inversion scheme that simultaneously retrieves total surface current vectors (TSCV) and ocean surface vector winds (OSVW). The results confirm the excellent performance of the EE11 SeaSTAR concept, with Root Mean Square Errors (RMSE) for TSCV and OSVW at 1 × 1 km² resolution consistently better than 0.1 m/s and 0.4 m/s, respectively. The analyses highlight some performance degradation in some relative wind directions, particularly marked at near range and low wind speeds. Retrieval uncertainties are also reported for several variations around the SeaSTAR baseline three-azimuth configuration, indicating that RMSEs improve only marginally (by ∼0.01 m/s for TSCV) when including broadside Radial Surface Velocity or broadside dual-polarisation data in the inversion. In contrast, our results underscore (a) the critical need to include broadside Normalised Radar Cross Section data in the inversion; (b) the rapid performance degradation when broadside incidence angles become steeper than 20° from nadir; and (c) the benefits of maintaining ground squint angle separation between fore and aft lines-of-sight close to 90°. The numerical results are consistent with experimental performance estimates from airborne data and confirm that the EE11 SeaSTAR concept satisfies the requirements of the mission objectives. Full article

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. Full article

Over several decades, high-frequency (HF) radars have been employed for remotely measuring various ocean surface parameters, encompassing surface currents, waves, and winds. Wind direction and speed are usually estimated from both first-order and second-order Bragg-resonant scatter from two or more HF radars monitoring the same area of the ocean surface. This limits the observational domain to the common area where second-order scatter is available from at least two radars. Here, we propose to estimate wind direction and speed from the first-order scatter of a single HF radar, yielding the same spatial coverage as for surface radial currents. Wind direction is estimated using the ratio of the positive and negative first-order Bragg peaks intensity, with a new simple algorithm to remove the left/right directional ambiguity from a single HF radar. Wind speed is estimated from wind direction and de-tided surface radial currents using an artificial neural network which has been trained with in situ wind speed observations. Radar-derived wind estimations are compared with in situ observations in the Lower Saint-Lawrence Estuary (Quebec, Canada). The correlation coefficients between radar-estimated and in situ wind directions range from $0.84$ to $0.95$ for Wellen Radars (WERAs) and from $0.79$ to $0.97$ for Coastal Ocean Dynamics Applications Radars (CODARs), while the root mean square differences range from 8° to 12° for WERAs and from 10° to 19° for CODARs. Correlation coefficients between the radar-estimated and the in situ wind speeds range from $0.89$ to $0.93$ for WERAs and from $0.81$ to $0.93$ for CODARs, while the root mean square differences range from 1.3 m.s⁻¹ to 2.3 m.s⁻¹ for WERAs and from 1.6 m.s⁻¹ to 3.9 m.s⁻¹ for CODARs. Full article

Sea surface wind, waves, and currents are the three basic parameters that describe the dynamic process of sea surface, and they are coupled with each other. To more accurately describe large-scale ocean motion and extract the ocean dynamic parameters, we adopt the spaceborne Doppler measurement to estimate the radial Doppler velocity generated by the sea surface motion. Due to the presence of wind and waves, the Doppler spectrum will be formed, shifted and broadened. Pulse-pair phase interference is used to obtain the Doppler spectrum from the sea surface echo. We simulate the Doppler spectrum with different look geometry and ocean states in a spaceborne condition. In this paper, the Doppler centroid variations are estimated after reducing the platform Doppler velocity under different observation conditions. With the increase in wind speed, the measured Doppler shift increases, and the simulated Doppler centroid accuracy is estimated. In addition, the measurement error along the trace direction is at the maximum, and the error in the cross-track is the smallest. At moderate wind-wave conditions, the Doppler velocity offset can be less than 0.1 m/s. Full article

Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The SSS fronts have been identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS, computed using the Belkin–O’Reilly algorithm (BOA). The SSS gradient fields generated by the BOA have been log-transformed to facilitate feature recognition. The log-transformation of SSS gradients markedly improved the visual contrast of gradient maps, which in turn allowed new features to be revealed and previously known features to be documented with a monthly temporal resolution and a mesoscale (~100 km) spatial resolution. Monthly climatologies were generated and analyzed for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela Upwelling. A 2000 km-long triangular area between Africa and Brazil was found to be filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100 km wave length. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of meridional SSS maximum (Smax) was documented. The meridional Smax belt shifts north–south seasonally while retaining its well-defined linear morphology, which is suggestive of a yet unidentified mechanism that maintains this feature. The Subtropical Frontal Zone (STFZ) consists of two tenuously connected fronts, western and eastern. The Brazil Current Front (BCF) extends SE between 40 and 45°S to join the subantarctic front (SAF). The STFZ trends NW–SE across the South Atlantic, seemingly merging with the SAF/BCF south of Africa to form a single front between 40 and 45°S. In the SW Atlantic, the Rio de la Plata plume migrates seasonally, expanding northward in winter (June–July) from 39°S into the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The inner Plata front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter (June–July) from 53°S up to 39–40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the Angola–Benguela Front at 16°S. The Benguela Upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela Upwelling water forms a salinity front, which is separate from the thermal Angola–Benguela Front at 16°S. The high-salinity tropical water (“Angola water”) forms a wedge between the low-salinity waters of the Congo River outflow and Benguela Upwelling. This high-salinity wedge is bordered by salinity fronts that migrate north–south seasonally. Full article

High-frequency radars (HFRs) are important for remote sensing of the marine environment due to their ability to provide real-time, wide-coverage, and high-resolution measurements of the ocean surface current, wave height, and wind speed. However, due to the intricate multidimensional processing demands (e.g., time, Doppler, and space) for internal data and effective suppression of external noise, conducting quality control (QC) on radar-measured data is of great importance. In this paper, we first present a comprehensive quality evaluation model for both radial current and synthesized vector current obtained by direction-finding (DF) HFRs. In the proposed model, the quality factor (QF) is calculated for each current cell to evaluate its reliability. The QF for the radial current depends on the signal-to-noise ratio (SNR) and DF factor of the first-order Bragg peak region in the range–Doppler (RD) spectrum, and the QF for the synthesized vector current can be calculated using an error propagation model based on geometric dilution of precision (GDOP). A QC method is then proposed for processing HFR-derived surface current data via the following steps: (1) signal preprocessing is performed to minimize the effect of unwanted external signals such as radio frequency interference and ionospheric clutter; (2) radial currents with low QFs and outliers are removed; (3) the vector currents with low QFs are also removed before spatial smoothing and interpolation. The proposed QC method is validated using a one-month-long dataset collected by the Ocean State Monitoring and Analyzing Radar, model S (OSMAR-S). The improvement in the current quality is proven to be significant. Using the buoy data as ground truth, after applying QC, the correlation coefficients (CCs) of the radial current, synthesized current speed, and synthesized current direction are increased by 4.33~102.91%, 1.04~90.74%, and 1.20~62.67%, respectively, and the root mean square errors (RMSEs) are decreased by 2.51~49.65%, 7.86~27.22%, and 1.68~28.99%, respectively. The proposed QC method has now been incorporated into the operational software (RemoteSiteConsole v1.0.0.65) of OSMAR-S. Full article

The Tongbai Mountains are an ecologically sensitive region to climate change, where there lies a climatic transitional zone from a subtropical to a warm–temperate monsoon climate. The northern boundary of Pinus taiwanensis Hayata is here; thus, climate information is well recorded in its tree rings. Based on developed earlywood width (EWW), latewood width (LWW) and total ring width (RW) chronologies (time period: 1887–2014 year) of Pinus taiwanensis Hayata in the Tongbai Mountains in central China, this paper analyzed characteristics of these chronologies and correlations between these chronologies and climate factors. The correlation results showed that earlywood width chronology contains more climate information than latewood width chronology and total ring width chronology, and mean temperature and mean maximum temperature in May–June were the main limiting factors for radial growth of Pinus taiwanensis Hayata. The highest significant value in all correlation analyses is −0.669 (p < 0.05) between earlywood width chronology and May–June mean temperature (T_MJ) in the pre-mutation period (1958–2005) based on mutating in 2006. Thus, this paper reconstructed May–June mean temperature using earlywood width chronology from 1901 to 2005 (reliable period of earlywood width chronology is 1901–2014). The reconstructed May–June mean temperature experienced eight warmer periods and eight colder periods and also showed 2–3a cycle change over the past 105 years. The spatial correlation showed that the reconstructed series was representative of the May–June mean temperature variation in central and eastern China and significant positive/negative correlation with the sea surface temperature (SST) of the subtropical Pacific Ocean and the tropical Western Pacific Ocean and Indian Ocean from the previous October to the current June. This also indicated that May–June mean temperature periodic fluctuations might be related to the quasi-biennial oscillation (QBO) in the tropical Western Pacific Ocean and Indian Ocean. The results of this study have extended and supplemented the meteorological records of the Tongbai Mountains and have a guiding significance for forest tending and management in this area. Full article

Marine radars are increasingly popular for monitoring meteorological and oceanographic parameters such as ocean surface wind, waves and currents as well as bathymetry and shorelines. Within this paper a coherent on receive marine radar is introduced, which is based on an incoherent off the shelf pulsed X-band radar. The main concept of the coherentization is based on the coherent on receive principle, where the coherence is achieved by measuring the phase of the transmitted pulse from a leak in the radar circulator, which then serves as a reference phase for the transmitted pulse. The Doppler shift frequency can be computed from two consecutive pulse-pairs in the time domain or from the first moment of the Doppler spectrum inferred by means of a short time Fast Fourier Transform. From the Doppler shift frequencies, radial speed maps of the backscatter of the ocean surface are retrieved. The resulting backscatter intensity and Doppler speed maps are presented for horizontal as well as vertical polarization, and discussed with respect to meteorological and oceanographic applications. Full article

The Doppler scatterometer is a new style of remote sensing tool that can provide current measurements over a wide swath for rapid global coverage. The existing current estimation method for Doppler scatterometry uses the maximum likelihood method to jointly derive the wind and current fields but shows high computational complexity. Moreover, the current radial speeds measured along two arbitrary observation azimuths are used to derive the vector current according to the parallelogram rule, which is not applicable for the case where two observation azimuths are not perpendicular. In this paper, a vector current velocity inversion method using an optimally selected observation azimuth combination—as well as a general current velocity calculation method—is proposed for Doppler scatterometry. Firstly, current radial speeds along several different observation azimuths are estimated using an interferometric phase difference matching method with low computational complexity. Then, two current radial components of each point are arbitrarily selected to estimate a preliminary current direction using the proposed vector current velocity derivation method. Finally, two observation azimuths that have the smallest intersection angles with the preliminarily estimated current direction are selected for vector current velocity determination. With the Ocean Surface Current Analyses Real-time (OSCAR) data as current input, vector current estimation experiments were conducted based on simulation analysis using an instrument conceptual design model for a pencil-beam scatterometer. The results show that the standard deviation of the estimated current velocity magnitude is 0.06 m/s. Compared with the reported results obtained by the existing method, the inversion accuracy of velocity magnitude is improved by 67%. Full article