Spaceborne synthetic aperture radar (SAR) provides high-resolution sea surface observations to characterize oceanic and atmospheric variability at submesoscales. Despite significant progress in retrieving wind fields from SAR images, challenges remain particularly for single-antenna measurements where wind speed inversion often requires an external wind direction input. This study introduces and evaluates the use of directional features derived from SAR image sublook cross-spectra to improve wind retrieval. Specifically, we explore the imaginary part of SAR mean cross-spectra (IMACS) and its differential counterpart,$\Delta $IMACS, which capture directional signatures of range-traveling intermediate wind waves. Using systematically collocated Sentinel-1A wave mode SAR images and model winds, we demonstrate that IMACS exhibits a cosine-like dependence on wind direction at given wind speeds at two incidence angles, while$\Delta $IMACS reveals unique sensitivities that complement radar backscattering and can resolve wind direction ambiguities. A novel cost function combining radar backscattering, IMACS, and$\Delta $IMACS is proposed to retrieve wind vectors directly from SAR data without external wind direction inputs. The proposed scheme is evaluated through comparisons with independent model outputs and buoy measurements, showing improved performance. These findings highlight the potential of IMACS and$\Delta $IMACS to enhance self-sufficient wind inversion, offering a promising pathway for single-polarized SAR wind retrieval.

The surface wave investigation and monitoring (SWIM) instrument, onboard China and France Oceanography Satellite (CFOSAT), is a Ku-band real-aperture radar, with six beams that illuminate the ocean surface at near-nadir incidences ranging from 0° to 10°. In this study, we investigate the effect of rain on the normalized radar cross section (NRCS) measured by SWIM under both tropical cyclone (TC) and non-TC conditions. Rain primarily attenuates the radar backscatter from the ocean surface. Under non-TC conditions, when wind speeds are below 21 m/s, the NRCS reduction is small (4–5 dB) under heavy rain (>15 mm/h) conditions. The impact of rain generally decreases as wind speed increases, especially at the smallest incidence angle. At low wind speeds, the NRCS reduction is also sensitive to the incidence angle. Based on a simplified model and the differing sensitivities of NRCS to surface roughness at two near-nadir incidence angles, we find that rain also influences the surface signal at incidence angles below 10°, in addition to the dominant effect of atmospheric attenuation, which leads to a consistent NRCS reduction across all wind speeds and incidence angles. Specifically, it contributes positively to NRCS at wind speeds above 7 m/s and negatively at lower wind speeds. This behavior is attributed to increased surface roughness from splashes and ring waves at low wind speeds, and to decreased roughness due to wave damping at moderate and high wind speeds. In TC environments, the NRCS tends to saturate under light to moderate rain rates (15 mm/h) conditions, the NRCS continues to decrease as wind speed increases. In both TC and non-TC conditions, beyond atmospheric attenuation, the impact of rain on surface roughness must be considered to fully explain the observed NRCS variations with wind and rain rate.

The monitoring of waves at the ocean surface is critical for both operational needs (e.g., maritime traffic) and scientific studies (e.g., air-sea interactions). Synthetic aperture radar (SAR) Satellites provide one of the only remote sensing observations to retrieve ocean wave information on a global scale. However state-of-the-art SAR processing schemes often lead to poor inversion performance due to overly-simplistic assumptions. Here we leverage deep learning schemes to address these shortcomings. We state the targeted measurement of the ocean wave spectrum at sea surface as a neural mapping from SAR satellite observations. We exploit supervised deep learning schemes trained from a large-scale collocation dataset between real SAR observations and Wavewatch III model data. Our results emphasize for the first time how deep learning schemes can outperform the state-of-the-art analytical SAR-based inversion with an improvement in terms of mean square error greater than 65%. We analyse and discuss further the key features of the trained neural processing.

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.

Sea ice plays a pivotal role in the Earth’s climate system and exhibits high sensitivity to shifts in temperature and atmospheric conditions. The precise and timely assessment of sea ice parameters is essential for comprehending and forecasting the climate changes. However, the vast volume of satellite data covering ice-covered regions is impractical to be subjectively assessed. Hence, the utilization of automated algorithms becomes mandatory to fully exploit the continuous data streams from satellites. In this paper, we propose a UNet transformer-based architecture, called UT-MHTA, to sea ice segmentation using SAR satellite imagery. Our UT-MHTA network replaces the conventional multi-head attention (MHA) block with a multi-head transposed attention (MHTA) which can capture long-range pixel interactions, while still remaining suitable for large images. Our method demonstrates superior performance compared to state-of-the-art methods, without drastically raising the computational complexity. In particular, UT-MHTA achieves a mean intersection over union (mIoU) of 68.76% on the AI4Arctic data set, with an inference time of 865ms for a 400 km2product.

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.

This is the first presentation of quasi-synchronous spaceborne synthetic aperture radar (SAR) high-resolution images acquired from C-band Radar Constellation Mission (RCM) and RADARSAT-2 consisting of quad-polarization (HH + HV + VH + VV) wide-swath observations of Hurricane Epsilon. These measurements clearly show that the denoised HV- and VH-polarized normalized radar cross sections (NRCSs) have great consistency. NRCS values at HV- and VH-polarizations are more sensitive to wind speeds and less sensitive to incidence angles or wind directions than those at HH and VV for hurricane force winds. For large incidence angles and high wind speeds, the sensitivity of HH-polarized NRCS to wind speed is higher than that of VV. HH- and VV-polarized NRCSs gradually lose wind direction dependency at high winds. It is notable that the time interval between the two SAR acquisitions is only 3 min. This allows for a direct comparison of HV- and VH-polarized images to investigate the variations in high-resolution backscattering within the hurricane vortex, thereby revealing the most dynamical areas. An asymmetric dynamic is observed around the eye of Hurricane Epsilon, based on positive and negative differences (VH–HV) in the western and eastern parts of the eye. The impacts of rain on quad-polarized NRCS are also examined using collocated rain rates from the Global Precipitation Mission (GPM) and wind speeds from the Soil Moisture Active Passive (SMAP). Significant rain-induced NRCS attenuations are about 1.7 dB for HH and VV, and 2.2 dB for HV and VH, when the rain rate is 20 mm/h. These attenuations are associated with rain-induced turbulence and atmospheric absorption. This work shows that the collocated RCM and RADARSAT-2 hurricane observations provide a unique analysis of synoptic and joint C-band measurements of the ocean surface in quad-polarization; this is noteworthy in view of preparations for the next generation of dual-polarization scatterometer (SCA) onboard second-generation meteorological operational satellite program (MetOp-SG).

Since the first method proposed by Dvorak in the 70's, Tropical Cyclone (TC) monitoring strategy has been routinely improved for both operational analysis and forecasting of tropical cyclone intensity. However, one of the most widely used techniques for operational TC intensity analysis remains largely subjective, dependent on analyst training and lacking in incorporation of imagery beyond visible and infrared window frequencies. Using high-resolution data from the Sentinel-1 Synthetic Aperture Radar (SAR) mission, this study explores a Deep Learning approach to detect the eye-center of tropical cyclones and to estimate their intensity based on topology patterns. Moreover, we apply Gradient-based Class Activation Maps to better understand the characteristics of this learning based method. Experimental results demonstrate that the suggested approach not only recognizes TCs effectively, but also reveals potential to locate their centers accurately and compete in performance with existing subjective and automated intensity estimation techniques.

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.

How strong does the wind blow in a hurricane? This proves a question that is difficult to answer, but has far-reaching consequences for satellite meteorology, weather forecasting and hurricane advisories. In the EUMETSAT CHEFS project, KNMI, ICM and IFREMER worked with international colleagues to address this question to prepare for the EPS-SG SCA scatterometer, which introduces C-band cross-polarization measurements to improve the detection of hurricane-force winds. To calibrate the diverse available satellite, airplane and model winds, in-situ wind speed references are needed. Unfortunately, these prove rather inconsistent in the wind speed range of 15 to 25 m/s, casting doubt on the higher winds too. Should we trust dropsondes at high and extreme winds or perhaps put more confidence inthe moored buoy references? This dilemma will be presented to initiate a discussion with the international community gathered at IGARSS ‘21.

A new HH-polarized geophysical model function (GMF), called CMODH, has been proposed recently for C-band synthetic aperture radar (SAR) ocean surface wind speed retrieval. CMODH has the potential to be directly used to retrieve wind speed from HH-polarized SAR images without converting the normalized radar cross section (NRCS) from HH- into VV-polarization using various empirical and theoretical polarization ratio (PR) models. However, the capability of CMODH for wind speed retrieval has not been comprehensively validated. In this letter, as a case study, two C-band HH-polarized SAR images acquired in RADARSAT-2 (RS-2) quad-polarization and Sentinel-1A (S1-1A) dual-polarization modes are first used to examine the CMODH performance. The wind speeds estimated using CMODH are shown to be consistent with buoy winds. A statistical comparison is then carried out to further validate CMODH using collocated 1457 C-band RS-2 and 284 S1-1A/B HH-polarized SAR images and 110 in situ buoys. The results show that the CMODH-retrieved wind speeds are in good agreement with buoy measurements, with a bias of 0.07 and 0.49 m/s and a root mean square error (RMSE) of 1.66 and 2.05 m/s for RS-2 and S1-1A/B, respectively. Compared to hybrid models, such as CMOD5.N and various PR models, CMODH achieves the smallest RMSE for wind speed retrieval. Moreover, for the first time, it is shown that the wind speed retrieval capability of CMODH is better than that of CMOD5.N for incidence angles between 30° and 49° and wind speeds between 10 and 20 m/s, which provides a new prospective on wind retrieval improvement using a combination of VV- and HH-polarized SAR observations.

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.

The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world's oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance.

Sentinel-1 SAR Wave Mode (WV) contributes to the global ocean wave (wind sea and swell) measurements upon the ERS-1&2 and Envisat ASAR. It is a wave product that contains image and ocean spectra, as well as certain wave parameters, which is valuable observational data for global ocean wave studies. In this study, we focused on the characteristics of ocean swell generated by the Tropical Cyclone (TC) and their relations with TC properties. To this end, the Firework method was adopted to track the TC generated swells. Swell data with low quality or unmatched with the expected waves are filtered out. This quality control procedure was performed for each acquired track and relied on the expected swell consistency between successive acquisitions along any given track. The preliminary analysis involves LANE TC cases and S1A&B WV observations were collected. The results showed clear swell asymmetry emerging at the front and the rear of TCs. The wavelength asymmetry at forwarding propagation discretion and the opposite direction was determined by wave development in the front area. Maximum wavelength asymmetry could be found in the medium intensity and translation speed TCs. The data confirm the previous measurement that fetch-law could be used to the represent such wavelength asymmetry.

The Chinese-French oceanography satellite, CFOSAT, was launched on October 2018. Two Ku-band scatterometers are on-board: SCAT for the wind observation and SWIM for the wave observation. This paper presents the most recent results on the SWIM data quality analysis a few months after the end of the CAL/VAL phase.

Under extreme weather conditions, the imprints of kilometer-scale marine atmospheric boundary layer roll vortices on the ocean surface are clearly visible in synthetic aperture radar (SAR) images of storms. Therefore, information about wind direction in storms can be obtained by analyzing SAR image features caused by boundary layer rolls. VH-polarized SAR imagery captures the structural features of storms well and shows prominent image gradients along the radial directions of the storm. The signal-to-noise ratios of VH-polarized images are small in low wind speed areas, but they are large in the same regions of VV-polarized images. Also, the capability of retrieving the atmospheric rolls orientation in VV-polarization is found to be sensitive to incidence angle, with better performances for larger incidence angles. Thus, there is the potential to retrieve the storm's wind directions using a combination of the VH- and VV-polarized SAR observations. In this article, we use the local gradient method to estimate tropical cyclone (TC) wind directions from C-band RADARSAT-2 and Sentinel-1A dual-polarization (VV + VH) SAR imagery. As a case study, wind directions with a spatial resolution of 25 km are derived by using both wide-swath VV- and VH-polarized SAR imagery over two hurricanes (Earl and Bertha) and one Typhoon (Meranti). We compare wind directions derived from ten dual-polarization SAR images with collocated wind directions from buoys, Global Positioning System (GPS) dropsondes, scatterometer, and radiometer. Statistical comparisons show that the wind direction bias and root-mean-square error are, respectively, -0.54° and 14.78° for VV-polarization, 0.38° and 14.25° for VH-polarization, 0.20° and 13.30° for VV- and VH-polarization, suggesting dual-polarization SAR is more suitable for the estimation of TC wind directions than VV- or VH-polarization SAR.

In Winter 2017, the University of Massachusetts Amherst's Imaging Wind and Rain Airborne Profiler (IWRAP) was flown on a National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter aircraft under the direction of scientists from Center for Satellite Applications and Research (STAR) at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) over the North Atlantic ocean out of Shannon, Ireland. IWRAP is a dual-frequency, conically scanning, profiling Doppler radar initially developed by Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts Amherst that is routinely installed on the NOAA WP-3D research aircraft. The flight on February 6, 2017, targeted a region of high winds (greater than 30 m/s) that was also observed by the Sentinel-1B satellite's synthetic aperture radar. Sentinel-1B was configured to observe in extended wide swath mode in both VV- and VH-polarizations, whereas the IWRAP C-band radar was configured to measure all of VV-, VH-, and HH-polarizations. IWRAP and Sentinel-1B VV and VH normalized radar cross section (NRCS) at the same Earthincidence angle along the flight path match reasonably well during the entire flight, but some additional trends between aircraft and satellite can be observed. IWRAP VV-polarized NRCS generally match the CMOD5.h geophysical model function (GMF), suggesting errors in the Sentinel-1B processing chain.

Co- and cross-polarized geophysical model function (GMFs) both have limitations to retrieve ocean surface wind speeds. The former underestimates high winds and the latter overstimates low winds. In this study, we both use co- and cross-polarized SAR observations to retrieve hurricane wind speeds and directions. The local gradient method is first used to estimate hurricane wind directions from C-band RADARSAT-2 and Sentinel-1A dual-polarization SAR imagery. We compare wind directions derived from 10 dual-polarization SAR images with collocated wind directions from buoys, GPS dropsondes, scatterometer, radiometer, and Hurricane Research Division Real-time Hurricane Wind Analysis System (H*Wind) data. Statistical comparisons show that the wind direction bias and root-mean-square error are 3.22° and 21.53°, respectively. Subsequently, as a case study for Hurricane Maria, the retrieved wind directions, along with co- and cross-polarized GMFs are used to derive wind speeds. The retrieved wind speeds are validated with collocated Stepped-Frequency Microwave Radiometer (SFMR) measurements. The results show that the combination of co- and cross-polarized GMFs has capability of obtaining more reasonable hurricane wind speeds and directions than by only using co- or cross-pol GMF.

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.

C-band high resolution radar (SAR, for Synthetic Aperture Radar) is the only space-borne instrument able to probe at very high resolution and over all ocean basins the sea surface under extreme conditions. As part of the space component of the European Copernicus program dedicated to Earth observation, the two C-band SAR on board the Sentinel-1 constellation routinely provide high resolution acquisitions in wide swath. Sentinel-1 A & B operate according to acquisition plans defined in the HLOP (High Level Operation Plan) and regularly updated. This paper aims to show how plan can be modified on short notice allows to capture Tropical Cyclones (TC) over the oceans before landfall including open ocean and coastal areas. Examples of Sentinel-1 derived TC parameters used by the science community and TC forecaster in WMO operational TC centres are presented. The benefit of using Sentinel-1 acquisitions in combination with other missions is also discussed.

This paper presents the goal, organization and first results of the calibration and validation activities devoted to the Ku-band wave scatterometer SWIM in operation since the end of October 2018 after the launch of the China-France Oceanography SATellite CFOSAT.

Millions of wave mode synthetic aperture radar (SAR) images are routinely acquired by the ESA's two sentinel-1 satellites every month over the open ocean. These SAR images capture clear imprints of atmospheric boundary layer (ABL) roll vortices. This provides a new and unique opportunity to investigate the characteristics of ABL rolls globally and statistically. In this study, we take advantage of the deep learning classification tool that has been successfully developed to automatically identify ABL rolls. For each SAR image classified with ABL rolls, roll wavelength and orientation are extracted through spectral analysis. Surface meteorological variables are also collocated with each SAR image to address the atmospheric conditions of roll occurrence. Results show that roll vortices are prevalent over the whole ocean and mainly occur in unstable to near-neutral stratification. Roll characteristics follow the theoretical expectation and are in good agreement with previous studies.

Synthetic aperture radar (SAR) imagery is routinely acquired at HH-polarization in high-latitude areas for measuring surface wind over the ocean. However, in the contrary of VV-polarization, there is no HH-polarization geophysical model function (GMF) exists to directly retrieve wind speed from SAR images. In general, HH-polarized normalized radar cross section (NRCS) is thus converted into VV-polarization and then conventional CMOD functions are used with auxiliary wind direction information for wind speed retrieval. In this letter, we propose a new GMF for SAR ocean surface wind speed retrieval, called CMODH, which relates the C-band NRCS acquired at HH-polarization over the ocean, to the 10-m height wind speed, incident angle, and relative wind direction. We first use more than 220 000 ENVISAT ASAR radar backscatter measurements collocated with ASCAT winds to derive the CMODH coefficients. Subsequently, 1459 RADARSAT-2 (RS-2) and 428 Sentinel-1A/B (Sl-1A/B) HH-polarized SAR acquisitions under different wind speeds are matched to in situ buoy observations to validate CMODH. The statistical comparisons between SAR-observed and simulated NRCS show a bias of -0.07 dB and a root-mean-square error of 1.62 dB for RS-2, and -0.01 dB and 2.48 dB for S1-1A/B. These results suggest that the proposed CMODH has the potential to directly retrieve ocean surface wind speeds using C-band SAR images acquired at HH-polarization, with no need for NRCS transformation by using various empirical and theoretical polarization ratio models.

This paper provides improved quantitative estimates of the wind-ruffled roughness contributions to dual co- and cross-polarized radar signals. Expanding previous approaches, 1696 RADARSAT-2 quad-polarized synthetic aperture radar (SAR) measurements, co-located with 65 in situ National Data Buoy Center (NDBC) buoy observations, are analyzed. Considering all wind conditions, the impact of breaking and near-breaking waves on dual co- and cross-polarized radar signals is robustly documented. For VV polarized measurements, the contribution of breaking waves decreased from 60% to 20% with increasing incidence angle, whereas for HH polarization and cross-polarization measurements, it can amount to about 60%-70% for all incidence angles. Building on the large analyzed data set, robust empirical dependencies between breaking waves and their impact on co- and cross-pol signals are then derived, as functions of wind speeds, incidence angles, and azimuth directions.

Sentinel-1 (S-1) is a two-satellite constellation for continuity of operational synthetic aperture radar (SAR) observations. Wave mode (WV) is the default mode over open ocean for S-1 to monitor global ocean waves and wind field. Therefore, proper radiometric calibration is essential to accurately infer these geophysical quantities. Based on the global data set acquired by S-1A WV, assessment of normalized radar cross section (NRCS) is carried out through comparison with CMOD5.N predictions over open ocean. The calibration accuracy quantified by NRCS residuals between SAR measurements and CMOD5.N demonstrates distinct features for two incidence angles (23.8° and 36.8°). Particularly, NRCS at 23.8° is overall consistent with CMOD5.N, while NRCS at 36.8° displays great deviation. Two recalibration methods are then implemented by examining the backscattering profile over Amazon rain forest and ocean calibration. Both methods show the necessity for recalibration and obtain comparable correction factors for WV1 and WV2, respectively. The NRCS residuals by applying both methods are significantly reduced toward zero. By comparison, ocean calibration is more efficient and practical to implement.

Although basic understanding of the synthetic aperture radar (SAR) imaging mechanism of ocean waves has been achieved, challenges still remain. In this paper, a large number of quad-polarized SAR images are analyzed to help assess how the standard SAR imaging transformation applies to all polarization channels. Foremost, the azimuth cutoff, a parameter essentially governed by the detected wave motions, is today solely related to radar configuration and the ocean wave spectrum but not to the polarization configuration. As obtained, the analyses based on quad-polarized Radarsat-2 and Gaofen-3 products document the distinct dependence of azimuth cutoff on polarization and incidence angle. Especially for cross-polarized VH measurements, azimuth cutoff estimates are generally larger than copolarized HH ones, the latter already being larger than values estimated under VV configuration. This trend increases with the incidence angle. The systematic comparisons between SAR measurements and simulations further demonstrate that the present SAR nonlinear transformation may not properly take into account the differing coherence time associated with the multi-polarized observation of ocean scenes. In particular, to reproduce the large azimuth cutoff parameters of cross-polarized images, a reduced coherence time shall be expected. This measurable sensitivity shall enhance the capabilities of polarized SAR systems to precisely derive more ocean surface properties, especially the influence of wave breakers, by combining both the copolarization and cross-polarization measurements.

This paper assesses different retrieval schemes used for the Chinese Gaofen-3 Synthetic Aperture Radar (GF-3 SAR) co-polarized data. The data consist of 4186 GF-3 data points and collocated wind information from sources including the ASCAT scatterometer, HY2A-SCAT scatterometer, and National Data Buoy Center (NDBC) buoy wind data set. The VV-polarized geophysical model function (GMF) is a CMOD7 model while the HH-polarized GMF is a hybrid of the CMOD7 and PR model. Assessments involve comparisons between SAR-derived and collocated winds in terms of the root-mean-square difference (RMSD) and bias. First, a comparison between the two retrieval schemes for the VV-polarized data clearly shows that the optimal scheme performs better than the classical scheme for wind speed retrieval. Comparisons for HH-polarized data show similar results. These experiments indicate that the wind speed RMSDs for the GF-3 co-polarized data are within 2 m/s when using the optimal scheme. Moreover, the wind direction RMSDs from the two schemes have no significant difference, with values near 20°. Overall, these assessments indicate that the GF-3 co-polarized data are sufficient for operational wind speed retrieval using the optimal scheme. However, wind direction retrieval requires further improvement.

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.

Micro-wave radar backscatter signal depends on incidence angle. Along an ocean surface wave profile, the incident angle (relative to the local normal) of the emitted electromagnetic wave is modified according to the ocean surface wave slope (tilt effect). Consequently, highly spatially resolved radar measurements are modulated by the surface wave profile. On this basic principle, range resolved rotative micro-wave radars are about to be mounted on satellites to measure the directional ocean wave spectrum from space trough a Modulation Transfer Function (MTF). Three years of collocated near-nadir Normalized Radar Cross Section (NRCS) measurement from the Global Precipitation Measurements (GPM) with Wave Watch III (WW3) wave model are used to estimate the relationship between the sea state and the tilt MTF. At the incidence 10° and in Ku-band, azimuth angle relative to the wind direction impact on tilt MTF, is one of results featured in this article.

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.

The ability of Synthetic Aperture Radar (SAR) to observe two-dimensional ocean wave spectra is restrained by both nonlinear imaging and spatial resolution. The improvement of SAR spatial resolution allows us to investigate the properties of intermediate ocean waves. In this study, a new complex parameter focusing on intermediate ocean waves is defined based on SAR image cross-spectra to describe their statistical characteristics. It includes both the magnitude and directional information of intermediate ocean waves. At first order, they are found to be associated to wind speed and wind direction, respectively. The preliminary results show that imaginary part of this parameter exhibits approximately a linear relationship with radial wind speed. The global map of this parameter further confirms its correlation with surface winds, which is potential to better restrain the wind retrieval over one SAR imagette.

ESA's two Sentinel-1 satellites collect ~120,000 synthetic aperture radar wave mode vignettes every month over the world's oceans. This provides a new and unique opportunity for routine identification and study of a wide range of oceanic and atmospheric phenomena observed in SAR imagery. To this end, the first challenge is to develop an efficient and accurate method to detect and classify key geophysical phenomena signature among the whole dataset. In this study, the deep-learning-based convolutional neural network architecture of Inception v3 model was adopted. We identified 10 geophysical categories detectable by SAR and selected 320 Sentinel-l wave mode imagettes for each category. The full pre-trained Inception v3 model was then retrained using these images. Preliminary results demonstrate that this deep-learning methodology is quite effective, with overall accuracy each of the 10 classes exceeding 0.93 and clear class differentiation in cluster analysis. This opens perspectives to rely on wave mode vignettes in order to analyze geophysical phenomena at global scale. Further work remains to address ambiguous or unknown images that often include a mix of several air-sea processes.

Co-located over extreme events, C-band copolarized and cross-polarized normalized radar cross sections (NRCS) and L-band ocean surface roughness brightness temperature (TB,rough) are directly compared to analyze the similarities and differences between these two parameters at medium resolution (about 25 km). NRCS in VH-polarization and VV-polarization (σ0,VH, σ0,VV) were acquired by Sentinel-1 C-band synthetic aperture radar. TB,roughis estimated from brightness temperatures (TB) measured by the L-band radiometer on-board the Soil Moisture Active Passive mission. When the rain rate is less than 20 mm/h, a striking linear relationship is found between active C-Band cross-polarized NRCS and passive L-Band TB,rough: σ0,VH(θSAR) ∝ tan(θSAR) × TB,rough(θSMAP= 40°), without any apparent saturation for TB,roughranging from 3.5 to 17 K. Compared to both high TB,roughand σ0,VH, copolarized σ0,VVmeasurements saturate. As interpreted, this can correspond to a regime change of the air-sea interactions during extreme events. In heavy rain conditions, C-band co-polarized NRCS decreases for extreme situations. In these cases, the covariation between C-band cross-polarized NRCS and L-band TB,roughis less evident. An accurate and unambiguous assessment of the impact of rain will deserve further investigations.

Doppler radars at all incidence angles measure mean velocities and spreads that have complex relations to oceanic motions, with opportunities to measure winds, waves, and currents. Here, we extend previous theoretical models of backscatter and Doppler using a Kirchhoff approximation and the physical optics model. We show that in Ka-band, around 12° incidence, range-resolved measurements of Doppler and backscatter provide unambiguous estimations of the wave spectrum and surface current. This property is illustrated with numerical examples and airborne data from the Air Surface Water Ocean Topography instrument. The same measurement conditions can be exploited for global ocean mapping from the low Earth orbit sensor satellite configuration.

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.

With a low cut-off for the directional wave spectra CFOSAT will impact both mixed sea and swell. The complementary use of CFOSAT and SAR wave spectra will enhance the impact of the assimilation and therefore induce a more accurate sea state. The CFOSAT mission is on one hand a big challenge for operational wave forecasting system, and on the other hand a good opportunity for wave modellers in order to improve the source and dissipation terms in the wave model.

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.

Processing and analysis of Doppler information from Sentinel 1A Interferometric Wide (IW) and Extra Wide (EW) modes are performed for assessing the capabilities of mapping ocean surface current field. Data from Agulhas (South-Africa) and Norwegian Coast are used in combination with numerical models, higher-order satellite products, and Lagrangian drifters. Results show strong Doppler signal and dynamics from coastal areas caused by a mixture of surface current and wind/wave induced drifts at a spatial resolution of around 2 km2in IW mode and 4km2in EW mode. Doppler values of up to 70 Hz are observed, corresponding to a surface drift velocity of 3.5 m/s. The Sentinel 1 retrieved surface current component is in reasonable agreement with the circulation models and drifter measurements. Surface current values up to 1.5 m/s are observed in the central Agulhas current, with a standard deviation of around 0.39 m/s with respect to Lagrangian drifters.

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.

Global colocalized ocean surface measurements using the Global Precipitation Measurement near-nadir dual-frequency Ku- and Ka-band microwave measurements are analyzed and compared. Focusing on the Ka and Ku cross-sections fall-off with incidence angles, the contemporaneous measurements enable to more precisely document differing ocean scattering characteristics for both microwave frequencies. Sensitivity with wind speed and significant wave height is further reported using global comparisons with numerical estimates. As demonstrated, the bifrequency capability can provide direct means to efficiently separate short-scale wave contributions, between mean squared slope and curvature characteristics, and to further gain valuable insights concerning near-nadir instruments onboard future ocean satellite missions including the China-France Oceanography Satellite and the Surface Water Ocean Topography Mission.

Satellite ocean radar data are used to assess the flat surface reflectivity for seawater at 36 GHz by comparison to an existing model for dielectric constant variation. Sea surface temperature (SST) is the dominant control, and results indicate a 14% variation in the normalized radar cross section (NRCS) at Ka-band (35.75 GHz) that is in close agreement with model prediction. Consistent results are obtained globally using near-nadir incidence data from both the SARAL AltiKa radar altimeter and Global Precipitation Measurement mission rain radar. The observations affirm that small but systematic SST-dependent corrections at Ka-band may require consideration prior to NRCS use in ocean surface wave investigations and applications. As an example, we demonstrate a systematic improvement in AltiKa ocean wind speed inversions after such an SST adjustment. Lower frequency C- and Ku-band results are also assessed to confirm the general agreement with prediction and a much smaller variation due to SST.

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

Existing models for the short-wave spectrum of the sea surface are not consistent with microwave satellite data when multi-bands and multi-incidence data sets are considered. We devise a simple parametric model for the short-wave omnidirectional spectrum of the sea surface on the basis of a three-band (C, Ku, and Ka) and multi-incidence (low, moderate, and large) data set and an improved analytical scattering model, namely the non-Gaussian Weighted Curvature Approximation. This spectrum is also constrained by several optical measurements which provide a priori conditions on the total and filtered mean-square slopes. It is compared with classical models such as Elfouhaily and Kudryavtsev unified curvature spectra. Significant differences are observed at wave numbers corresponding to the range of decimeter scales. The new spectrum is by construction fully consistent with the omnidirectional normalized radar cross section of the multi-band data set.

Several algorithms have been proposed to retrieve near-surface wind fields from C-band synthetic aperture radar (SAR) images acquired over the ocean. They mainly differ in the way how to retrieve wind direction. Conventionally, the wind direction is taken from an atmospheric model or extracted from linear features visible on SAR images. Recently a new wind retrieval algorithm has been proposed by Mouche et al. (2012), which includes also the Doppler shift induced by motions of the sea surface. We have tested this algorithm on complex wind fields encountered over the Black Sea. It is shown that the new algorithm yields better near-surface wind fields than conventional wind retrieval algorithms.

So far, sea surface scattering polarization sensitivity is still difficult to fully understand. To gain understanding, full polarization C-band data spectral analysis is considered and the de-polarization (HV) and polarization ratio (PR) are jointly analysed with in-situ wind and wave observations.

The simulation and interpretation of microwave sea radar return in the near-nadir region are still issues in view of the limitations of the geometrical optics approximation and the multiscale and non-Gaussian nature of the surface. We show that an unambiguous and fully consistent physical approach can be reached in the framework of the physical optics. The model is developed on the basis of various satellite and airborne C-, Ku-, and Ka-band measurements using different reference surface roughness spectra. As found, the introduction of a peakedness correction based upon the excess kurtosis of slopes is necessary to obtain consistent analysis across the microwave frequency range. The model yields accurate simulations for the omnidirectional near-nadir normalized radar cross section in different frequency bands, provided the spectrum satisfies some a priori constraints on the distribution of the total and filtered slopes.

The synthetic aperture radar (SAR) Doppler centroid has been used to estimate the scatter line-of-sight radar velocity. In weak to moderate ocean surface current environment, the SAR Doppler centroid is dominated by the directionality and strength of wave-induced ocean surface displacements. In this paper, we show how this sea state signature can be used to improve surface wind retrieval from SAR. Doppler shifts of C-band radar return signals from the ocean are thoroughly investigated by colocating wind measurements from the ASCAT scatterometer with Doppler centroid anomalies retrieved from Envisat ASAR. An empirical geophysical model function (CDOP) is derived, predicting Doppler shifts at both VV and HH polarization as function of wind speed, radar incidence angle, and wind direction with respect to radar look direction. This function is used into a Bayesian inversion scheme in combination with wind from a priori forecast model and the normalized radar cross section (NRCS). The benefit of Doppler for SAR wind retrieval is shown in complex meteorological situations such as atmospheric fronts or low pressure systems. Using in situ information, validation reveals that this method helps to improve the wind direction retrieval. Uncertainty of the calibration of Doppler shift from Envisat ASAR hampers the inversion scheme in cases where NRCS and model wind are accurate and in close agreement. The method is however very promising with respect of future SAR missions, in particular Sentinel-1, where the Doppler centroid anomaly will be more robustly retrieved.

The ldquogalactic glitterrdquo phenomenon at L-band, i.e., the scattering of celestial sky radiation by the rough ocean surface, is examined here as a potential source of error for sea surface salinity (SSS) remote sensing. We begin by considering the transformations that must be applied to downwelling celestial noise in order to compute the eventual impact on the antenna temperature. Then, outside the context of any particular measurement system, we use approximate scattering models along with a model for the equilibrium wind wave spectrum to examine how the scattered signal at the surface might depend on the geophysical conditions and scattering geometry. It is found that, when the specular point lies far away from the galactic plane, where the incident celestial brightness is uniform, sea surface roughness has a negligible impact on the glitter. At such a point, variations in both the orientation of the incidence plane and the wind direction relative to the scattering azimuth have negligible impact. By contrast, when the specular point lies in the vicinity of a localized maximum of brightness, scattering by the roughened ocean surface may reduce the glitter by more than 30%, as compared to a perfectly flat surface, and the glitter amplitude may vary by up to 0.7 K with variations in wind direction and by up to 0.5 K with variations in incidence plane orientation. It is shown that accounting for the roughness impact on celestial noise contamination is of particular concern for the remote sensing of SSS.

Airborne radar observations of the sea surface at C- Band and small incidence angles are used to investigate some properties of the surface slope probability density function (pdf). The method is based on the analysis of the variation of the radar cross-section with incidence angle, assuming that the backscatter can be described by the Geometrical Optics theory. First, we show that roughness properties with scales larger than 12 cm can be analyzed in our configuration (C-Band, incidence 7 to 16deg). The radar data are then analyzed in terms of filtered mean square slope under the assumption of a Gaussian slope pdf. Dependence with wind speed of the upwind and total mss are analyzed and compared to various published studies. Finally an analysis of the radar data under a non-Gaussian assumption for the slope pdf is proposed, by applying the compound model suggested by [1].

We present an analysis of the azimuth anisotropy of the ocean radar backscatter from C-Band observations in VV and HH polarizations from the airborne radar STORM during the VALPARESO experiment. Comparisons with existing semi- empirical models show that none of them are in agreement with the anisotropy of the radar signal, and the trend with wind speed is too weak compared to the observations. This is attributed to a shortcoming in the description of the short surface waves anisotropy. By considering an additional mechanism for nonlinear wave interactions, we show that the short wave anisotropy is modified and can explain the anisotropy of the radar signal.

We propose here to use a combination of radar observations in HH and VV polarizations at C-Band to study the different contributions to the sea surface backscattering - polarized and non-polarized parts- and to characterize the relation between azimuth anisotropy of the signal and the surface properties.

This paper presents an analysis of measurements of the normalized radar cross-section (NRCS) in vertical and horizontal polarizations over the ocean obtained from the C-band airborne radar STORM. The dataset was collected during the experiment called "Validation with a Polarimetric Airborne Radar of ENVISAT SAR over the Ocean (VALPARESO)", which took place during the calibration/validation phase of the ENVISAT Advanced Synthetic Aperture Radar (ASAR). From this dataset, the properties of the polarization ratio are discussed and in particular its dependencies with radar geometry (incidence and azimuth angle) as well as with meteorological conditions (wind and sea state). The polarization ratio is found to be dependent on incidence and azimuth angles. Its dependence with incidence angle is found to be significantly different from empirical models previously proposed in the literature. It also exhibits some correlation with surface conditions (wind and wave) with a more important correlation with significant wave steepness. Two new analytical formulations are proposed to model the polarization ratio, one as a function of incidence angle only, the second one with additional dependence with azimuth angle. It is shown that it is necessary to consider an azimuth-dependent polarization ratio for incidence angles larger than 30/spl deg/. Comparisons with the polarization ratio from ENVISAT ASAR images are used to assess this model.