Currently, global directional wave spectra are routinely providing by synthetic aperture radar (SAR) on Sentinel-1 in Wave Mode and real aperture radar, i.e., SWIM onboard CFOSAT. However, SAR spectra suffer from azimuth cut-off effects, while SWIM spectra exhibit parasitic peaks and 180° ambiguity. Leveraging the complementary of these two satellite radars for directional spectra merging remains an open scientific challenge. In this letter, we propose a deeply-learned based 2D wave spectrum fusion model aiming to integrate CFOSAT SWIM and Sentinel-1A wave mode Level-2 products. After spatiotemporal collocating, and rejecting suspicious spectra, two-year period (June 2022 to June 2024) of 2D wave spectra triplets (S-1, SWIM, and ERA5) are used for the fusion model training. The deep learning merged spectra show good consistency with ERA5 benchmark regarding 2D spectra, the derived omni - directional energy distribution and the integrated wave parameters. Comparative analysis with ERA5 reanalysis demonstrates that our fused directional spectrum could effectively compensate for high-frequency information loss and spectral distortion in SAR products, meanwhile suppress SWIM's parasitic peaks and resolve directional ambiguity. Qualitatively, compared to ERA5, the Brüning’s correlation coefficient and square error of our fusion results reaches 0.95 and 0.17, significantly outperforming official SAR (0.79 and 0.58) and SWIM (0.83 and 0.52) products. The fusion mitigates SAR’s high-frequency losses and SWIM’s artifacts, enhancing ocean wave spectrum accuracy for improved geophysical applications.

Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring, etc. Ground-based weather radars, such as NOAA’s Next-Generation Radar (NEXRAD), provide reflectivity and precipitation estimates of rainfall events. However, their observation range is limited to a few hundred kilometers, prompting the exploration of other remote sensing methods, particularly over the open ocean, that represents large areas not covered by land-based radars. Here we propose a deep learning approach to deliver a three-class segmentation of SAR observations in terms of rainfall regimes. SAR satellites deliver very high resolution observations with a global coverage. This seems particularly appealing to inform fine-scale rain-related patterns, such as those associated with convective cells with characteristic scales of a few kilometers. We demonstrate that a convolutional neural network trained on a collocated Sentinel-1/NEXRAD dataset clearly outperforms state-of-the-art filtering schemes such as the Koch’s filters. Our results indicate high performance in segmenting precipitation regimes, delineated by thresholds at 24.7, 31.5, and 38.8 dBZ. Compared to current methods that rely on Koch’s filters to draw binary rainfall maps, these multi-threshold learning-based models can provide rainfall estimation. They may be of interest in improving high-resolution SAR-derived wind fields, which are degraded by rainfall, and provide an additional tool for the study of rain cells.

Precipitation measurement is an important prior for several operational and scientific applications, including weather forecasting, hazard prevention, agriculture, etc. Weather radars, such as NEXRAD, observe the air volume reflectivity and infer precipitation intensity at high resolution. However, their capabilities are limited over the ocean. C-band SAR imagery, which is sensitive to ocean surface roughness, is known to be sensitive to the effect of rain. In this study, we improve existing NEXRAD/Sentinel-1 collocations and train a U-Net deep learning model to estimate NEXRAD radar reflectivity from Sentinel-1 observations. Precipitation fore-casts are returned as segmentations with thresholds at 1, 3 and 10 mm/hr. The results indicate high performance over a wide range of wind speeds and thus can provide an accurate estimate of precipitation in the absence of weather radar.

Spaceborne synthetic aperture radar (SAR) has been used for years to estimate high-resolution surface wind field from the ocean surface backscattered signal. Current SAR platforms have one single fixed antenna, and traditional inversion/retrieval schemes rely on one copolarized channel, leading to an unconstrained optimization problem for providing independent estimates of wind speed and direction. For routine application, this is generally solved witha prioriinformation from the numerical weather prediction (NWP) model, inducing severe limitations for rapidly evolving meteorological systems where discrepancies can be significant between model and measurements. In this study, we investigate the benefit of having two simultaneous acquisitions with phase-preserving information in copolarization and cross polarization provided by Sentinel-1 (S-1). A comprehensive analysis of the co-cross-polarization coherence (CCPC) is performed to adequately estimate and calibrate CCPC values from S-1 interferometric wide (IW) mode images acquired over the ocean. A new polarimetric calibration (PolCAL) methodology based on least-squares (LS) criterion and direct matrix inversion is proposed yielding crosstalk estimates. We document CCPC odd symmetry with respect to relative wind direction for light to medium wind speeds (up to 14 m/s) and incidence angle from 30° to 45°. The azimuthal modulation is found to increase with both wind speed and incidence angle. An analytical model C-band polarimetric geophysical model function (CPGMF) is provided. The synergy of the CCPC with other radar parameters, such as backscattering coefficients or Doppler, to further constrain the inversion scheme is assessed, opening new perspectives for SAR-based wind field retrieval independent of any NWP model information.

Nowadays, Sentinel-1 (S-1) synthetic aperture radars (SARs) operating in wave mode and the real aperture radar (RAR) called Surface Waves Investigation and Monitoring (SWIM) onboard the China-France Oceanography SATellite (CFOSAT) are the only two kinds of spaceborne radars providing directional ocean wave information globally. To quantify the absolute uncertainties in the swell wave heights of a specific wave system (Hss) observed from these two spaceborne sensors, a triple colocation error model is exploited via WaveWatch III (WW3) wave model hindcasts for the first time. After implementing spatiotemporal collocation, cross-assigning swell partitions, and rejecting suspicious data, a database of the optimal-matched Hss triplets (S-1, SWIM, and WW3) is determined over a one-year period (June 2020 to June 2021). Qualitatively, traditional dual intercomparisons indicate the inconsistency between the Hss from both SAR and RAR radars in terms of systematic biases. Furthermore, the triple collocated error analysis quantitively reveals that, at a global scale, SWIM onboard CFOSAT has the least uncertainty in Hss (~0.2 m root-mean-square error (RMSE) and ~11% scatter index (SI)) compared with S-1 SAR (0.35-0.50 m RMSE and 17% - 26% SI depending on incidence modes) and WW3, under the assumption that the random errors of the three data sources are independent, indicating that the newly launched SWIM instrument is an invaluable resource of directional wave information for the scientific community. The results are discussed with respect to regional error characteristics along with a feasible explanation of error sources. The findings could be helpful for better understanding and synergistically exploiting the Hss datasets from these two spaceborne radars.

Segmentations of ocean SAR images (Sentinel-1 A and B) into 10 classes of metoceanic phenomena are for the first time presented, with a 400 m resolution. Ocean SAR images segmentation differs from classic deep learning problems with a high variety of shapes and a particular importance of high-frequency patterns. To this end, an assessment of deep learning frameworks is performed, with a focus on the comparison between weakly supervised and supervised methods. Metrics based on the Wassertein distance indicate best performances by the supervised segmentation (U-Net) given operational constraints, thus highlighting the significance of properly annotated data sets. While available training data sets are made of small$20 \times 20 \text{km}$imagettes, the extension of the inference from imagettes to wide swath images, with a wider variety of incidence angles, presents promising results and opens the way to more extensive oceanographic applications in SAR imagery.

Windrows signatures on SAR images are analyzed to estimate their orientation and their estimated accuracy with respect to reference wind direction provided by buoy and model. The accuracy dependency to several parameters of interest such as wind speed, processing internal variables, incidence angle, a priori presence of wind streaks but also SAR product acquisition modes and polarizations is analyzed. Regression models provide a satisfying method to combine these parameters and predict the a priori error on the estimated wind direction, crucial for any downstream application.

CYMS is an ESA-funded project aiming at scaling up an operational service for Tropical Cyclone monitoring, in view of its potential integration as part of a Copernicus Service. In 2020, the demonstration of such a service has been operated and user's requirements refined. More than 90 scenes of TC have been acquired worldwide with Sentinel-1 A, Sentinel-1 B and Radarsat-2 thanks to the late programming acquisitions. These images have been processed into ocean surface wind field and disseminated to the user community. The user feedback on the data confirms that the capabilities of SAR to probe the ocean surface at high resolution is unique and offer potential for science applications related to the analysis of the TC inner core structure, possibly bringing new insight on the processes within the eye. Those observations are also crucial over regions lacking any aircraft observation or ground-based meteorological Radars for monitoring and validating the cyclone forecasts. One of the main requirements is the need for Near Real-Time distributions of CYMS observations to allow their operational use. This is currently stated as a project but one of the objectives is to ensure that one of the Copernicus services hosts a service dedicated to Cyclone Observation to allow acquisitions on Cyclones in a Copernicus framework.

This paper presents a method to generate maps of offshore wind power at turbine hub height from spaceborne SAR data. Two techniques based on machine learning are presented. The first one can be trained with metocean buoys and the second one, more precise, requires on -site profiling Lidars. If Lidars are not available, SAR surface winds at 10m are improved with machine learning. They are then extrapolated at 40m with a classical power law, and then at higher altitudes with an atmospheric numerical model. If profiling Lidars are available, parameters from the numerical model are added as input to the machine learning algorithm and the training is performed directly at turbine hub height with the Lidar data. Once the wind at turbine hub height is obtained, the wind power is then calculated using a Weibull distribution. The resulting maps are compared with the outputs of the numerical model. The maps based on SAR data provide a much higher level of detail and a better estimation of the coastal gradient, which is important to optimize wind farm siting and available potential energy production. The accuracy of the wind power is found to be in the range ±5% compared to the Lidars.

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.

SAR ocean surface wind retrieval is generally based on the co-polarized Normalized Radar Cross Section (NRCS). Yet, since April 2014, Sentinel-1 TOPS-mode acquisitions enable large swath dual-pol measurements while preserving relative phase information. In this study, this new capability is analyzed via the co-cross coherence from a massive S-1 VV/VH IW dataset. A Polarimetric Calibration (POLCAL) methodology is proposed to calibrate this variable over sea surface. The odd symmetry of the co-cross coherence from S-1 is confirmed, and potential Polarimetric Geophysical Model Function (PGMF) can be now investigated. The integration of this PGMF in the wind field inversion scheme will be the next step.

In wave mode, Sentinel-1A/B from ESA provide swell spectra dataset on a continuous and global basis since. In this paper, a method of a dynamical validation approach for SAR swell spectra is developed, in which the Sentinel-1A swell spectra are partitioned and propagated up to the vicinity of insitu reference along the great circle based upon the linear wave theory. Here, sentinel-1A wave mode data for the period of three months were dynamically validated against buoys from NDBC and CDIP networks. Comparison results show a good agreement with buoy measurements. Influent of buoy types on the validation results are also discussed.

Sentinel-1 ‘s SARs operate since April 2014 (S1-A) and April 2016 (S1-B) and routinely acquire images over coastal and open waters. Compared to its predecessor ENVISAT/ASAR, Sentinel-1 SAR offers several improvements such as a better Wave Mode imagette coverage, more systematic dual-polarizations, a new TOPSAR acquisition mode over coastal regions and improved Doppler estimator allowing higher resolution Doppler grid. Based on these additional capabilities, many algorithmic and use case scenario improvements have been tested and validated to provide a more complete ocean state view and first direct assessment of extreme events. Instrument level issues like the accuracy of the satellite restituted attitude could also be highlighted.

During summer 2016, the European Space Agency (ESA) set up the Satellite Hurricane Observations Campaign, a campaign dedicated to hurricane observations with Sentinel-1 synthetic aperture radar (SAR) in both vertical-vertical (VV) and vertical-horizontal (VH) polarizations acquired in wide swath modes. Among the 70 Sentinel-1 passes scheduled by the ESA mission planning team, more than 20 observations over hurricane eyes were acquired and tropical cyclones were captured at different development stages. This enables us to detail the sensitivity difference of VH and VV normalized radar cross section (NRCS) to the response of intense ocean surface winds. As found, the sensitivity of the VH-NRCS computed at 3-km resolution is reported to be more than 3.5 times larger than in VV. Taking opportunity of SAR high resolution, we also show that the decrease in resolution (up to 25 km) does not dramatically change the sensitivity difference between VV and VH polarizations. For wind speeds larger than 25 m/s, a new geophysical model function (MS1A) to interpret cross-polarized signal is proposed. Both channels are then combined to get ocean surface wind vectors. SAR winds are further compared at 40-km resolution against L-band soil moisture active and passive mission (SMAP) radiometer winds with co-locations less than 30 min. Overall excellent consistency is found between SMAP and this new SAR winds. This paper opens perspectives for MetOp-SG SCA, the next-generation C-band scatterometer with co- and cross-polarization capability.

Sentinel-1's SAR instrument offers a number of improvements with respect to its predecessor ENVISAT/ASAR such as a much better Wave Mode imagette coverage, improved Doppler estimator allowing higher resolution Doppler grid, more systematic dual-polarizations and a new TOPSAR acquisition mode. In the context of SEOM program, the Sentinel-1 ocean study offers to take advantage of these new capabilities to improve the retrieval of ocean sea state parameters: surface wind fields, directional wave spectrum, total significant wave height and surface currents. The study also tackles the ability to conduct a synergetic retrieval scheme in which the mutual effects of sea state components are taken into account.

At the horizon 2020, 2-D ocean wave measurements from 4 different satellites will be available. It will be a world first. Anticipating the large number of available data and the specificity of each sensor, we will have to define new methodologies to combine all different observations. This will offer new opportunities to describe ocean waves at global scale with improved time-space resolutions and will open new perspectives to study ocean waves. This paper deals with the perspectives for combining and exploiting ocean wave spectra measured from different space missions such as Sentinel-1, CFOSAT and HY-3. An example of application is shown on waves generated by hurricane Kilo.

Crossing swell, is a complicated sea state characterized by the co-existence of swell systems generating from different sources. Although many investigations have focused on the global swell climatology, our understanding of global statistical distribution for the crossing swell is still limited so far. In this paper, we present a global view of crossing swell using 10-years Synthetic Aperture Radar (SAR) derived directional swell spectra from Envisat ASAR in Wave Mode from 2003 to 2011. In contrast to analyze the directly but occasionally SAR captured sea state of crossing swell, we employ an approach of propagating observed swell taking advantage of the internal consistency of swells. Results indicate three dominated crossing swell areas termed “crossing swell pools”, in Pacific, Atlantic and Indian Oceans. The pool in Atlantic Ocean shows a relative stable behavior for all seasons, in contrast to the one in Indian Ocean with seasonal occurrence and the one in Pacific Ocean shrinking during boreal summer. Forming and seasonal variation of the crossing swells are interpreted using the swell origins derived from ASAR Wave Mode.

This paper addresses the results of the instrument and product performance verification, radiometric and geometric calibration achieved during the since commissioning and routine phase.

Recently, with the availability of a great number of synthetic aperture radar (SAR) wave mode spectra, it has been possible to derive a set of great circle lines of swell propagation whose intersection points indicate the position of the storm generating the observed swell field. However, due to the inherent limitations of SAR spectra, the locus of convergence of great circle of swell propagation can be sometimes diffuse or contain multiple convergence regions. In this letter, we adapted the fuzzy cluster logic method to identify the regions of convergence of SAR wave field rays. The analysis of the results of the fuzzy algorithm clearly indicates the ability of this statistical method to identify the cluster center region of swell fields observed in SAR wave mode images. The measure of success of the method was how well the generation center of the swell could be traced back to an existing strong storm system.