A growing number of spaceborne radar sensors provide increasingly abundant collocated ocean measurements, raising new opportunities for synergetic remote sensing techniques. However, interpreting this diverse observational dataset requires novel approaches to consistently process different data types and invert geophysical fields. Although radar scatterometers continuously monitor ocean winds, joint estimation of speeds from various platforms remains challenging. Additionally, new instruments lack intercallibration algorithms and exhibit varying sampling and resolution characteristics. Thus, joint utilization of such multimodal data is not straightforward.We introduce a framework for synergistic ocean surface wind vector retrieval from heterogeneous radar measurements on multiple satellite platforms. A key point is deriving probabilistic geophysical model functions (PGMFs) mapping observed backscatter to wind speed/direction probability distributions. PGMFs encapsulate inherent backscatter variability from hidden factors. The methodology enables the combined utilization of various radar data and includes recalibration techniques. The wind inversion algorithm combines probabilities based on PGMF of each sensor. The key elements of the approach is demonstrated using CFOSAT and ASCAT data. Initial calibration enables the derivation of the sample PGMF and refinement of ambiguities. Ongoing efforts are to improve the accuracy of the PGMF, take into account non-wind effects, and evaluate multi-sensor performance. The flexible probabilistic technique promises improved wind vector estimation by exploiting various radar missions.

Spaceborne radar scatterometers require precise calibration to provide accurate sea surface wind speed retrievals. The Scatterometer (SCAT) onboard the China-France Oceanography Satellite (CFOSAT) presents additional calibration complexities as a rotating antenna instrument making moderate incidence angle dual-polarization backscatter measurements. Here we propose a two-step methodology for recalibrating SCAT backscatter measurements σ0. First, we match probability distribution functions of SCAT σ0to a standard geophysical model function at common wind speeds (7-8 m/s) to determine calibration coefficients as a function of incidence angle, antenna azimuth, and polarization. We then derive an instrument-specific Geophysical Model Function from the recalibrated σ0accounting for variations from the native antenna gain and noise characteristics.Validating SCAT wind retrievals from the recalibrated σ0against those using traditional fixed calibration shows significant improvements in accuracy over time, with reduced cross-track errors and decreased uncertainties for most wind speeds. The proposed recalibration technique enables precise wind retrievals from SCAT well before comprehensive engineering calibrations and provides a practical solution for overcoming unforeseen calibration issues to achieve high-accuracy satellite wind speed retrievals. This empirical two-step approach may be extensible to other innovative spaceborne scatterometer systems where signal callibration technics are not fully established.

The surface waves investigation and monitoring (SWIM) instrument onboard the China–France Oceanography Satellite (CFOSAT) can retrieve directional wave spectra with a wavelength range of 70–500 m. This study aims to validate the partitioned integrated wave parameters (PIWPs) from SWIM, including partitioned significant wave height (PSWH), partitioned peak wave period (PPWP), and partitioned peak wave direction (PPWD), against those from National Data Buoy Center (NDBC) buoys. With quasi-simultaneous spectra from two NDBC buoys 13 km away from each other near Hawaii, the methods of comparing PIWPs from two sets of spectra were discussed first. After cross-assigning partitions according to the spectral distance, it is found that wrong cross-assignments lead to many outliers strongly impacting the estimate of error metrics. Three methods, namely comparing only the best-matched partition, changing the threshold of spectral distance during cross-assignment, and maximum likelihood estimation of root-mean-square error (RMSE) of PIWPs, were used to reduce the impact of potential wrong cross-assignments. Using these methods, the SWIM PIWPs were validated against NDBC buoys. The results show that SWIM performs well at finding the spectral peaks of different partitions with the RMSE of PPWPs and PPWDs of 0.9 s and 20°, respectively, which can be a useful complement for other wave observations. However, the accuracy of PSWH from SWIM is not that good at this stage, probably because the high noise level in the spectra impacts the result of the partitioning algorithm. Further improvement is needed to obtain better PSWH information.

Our understanding of ocean circulation to date still bears many unanswered questions. Despite the development of a variety of methods for observing upper ocean current velocity at different scales, our knowledge of this subject has yet to be significantly improved. Historically, the drift of vessels led to the discovery of the oceanic surface current. The Automatic Identification System (AIS), originally intended to avoid collision in maritime traffic, contains the necessary information to retrieve vessel drift due to the surface current. Thus, the AIS data can be processed to derive sea-surface current components for any region in which there is maritime traffic. A dedicated inversion scheme has recently been theoretically formulated and implemented. In this study, we report the preliminary results obtained during an experimental campaign conducted aboard the oceanographic vessel Atalante, in the strait of Gibraltar, in October 2020. A map of oceanic circulation was calculated using the drift of all vessels emitting AIS messages within the strait and validation was achieved with the help of an Acoustic Doppler current profiler (ADCP) aboard the Atalante.

The CFOSAT mission provides a unique data set of collocated nadir, near-nadir and moderate angle dual-polarization Ku-band radar measurements. These combined measurements open new opportunities to revisit existing interpretation and approaches to analyze ocean radar backscatter signals. In particular, actual scatterometer wind vector retrieval algorithms and definitions of Geophysical Modulation Functions (GMFs) can be revisited, including sensitivity to additional parameters: wave state, currents, etc. The present work describes an alternative GMF approach based on the analysis of signal polarization differences, and interpretation in terms of ocean surface wind-roughness spectra. Based on CFOSAT measurements, the polarization-difference GMF reproduces main properties of standard empirical radar GMFs. However, it is more directly related to a theoretical short wave spectral model. This approach naturally enables the inclusion of additional sea state variables. Results of this work is intended to be implemented in wind retrieval processors to complement existing methods.

This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70-500 m] is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented.

CFOSAT (the China France Oceanography Satellite) is the joint mission from the Chinese and French Space Agencies to jointly perform global ocean surface wind and sea state observation and monitoring. The satellite will carry two Ku-band radars: the wave scatterometer (SWIM) and the wind scatterometer (SCAT). Such a unique configuration provides multi-look and multi-angle observations to reconstruct key parameters of local sea surface conditions: the directional wave spectrum and the determination of the sea surface wind speed and direction. From unique collocated wave and wind measurements, each single instrument observation by its own can benefit from the other one. As often discussed, radar returns, at the same location but for different angles of observations, will possibly exhibit different sensitivity with regard to various sea surface parameters, i.e. near-surface wind speed and direction, significant wave height, sea state degree of development and/or local swell wave spectrum characteristics. This often results in radar signal dispersion which limits the precision of the retrieval algorithms from radar remote sensing measurements. Accordingly, the potential to benefit from additional information from a co-located second instrument could be strongly useful in the correction of such factors. The present work considers the possibility of using the near-nadir SWIM measurements to improve the wind estimates from the scatterometer inversion algorithm. Preliminary estimates and simulations confirm that some types of errors, e.g. due to SCAT antenna geometry and configuration, can largely be reduced. As well, additional improvement can be expected for the measurements under low-wind and rapidly changing sea conditions.

We propose here a method to identify surface film in SAR images using the Alternating Polarization ratio images from ENVISAT. This ratio is lower in polluted areas than in non-polluted areas due to the difference in relative contributions of the non-Bragg scattering to the total radar signal.