Neural Ordinary Differential Equations (NODEs) serve as continuous-time analogs of residual networks. They provide a system-theoretic perspective on neural network architecture design and offer natural solutions for time series modeling, forecasting, and applications where invertible neural networks are essential. However, these models suffer from slow performance due to heavy numerical solver overhead. For instance, a popular solution for training and inference of NODEs consists in using adaptive step size solvers such as the popular Dormand-Prince 5(4) (DOPRI). These solvers dynamically adjust the Number of Function Evaluations (NFE) as the equation fits the training data and becomes more complex. However, this comes at the cost of an increased number of function evaluations, which reduces computational efficiency. In this work, we propose a novel approach: making the parameters of the numerical integration scheme trainable. By doing so, the numerical scheme dynamically adapts to the dynamics of the NODE, resulting in a model that operates with a fixed NFE. We compare the proposed trainable solvers with state-of-the-art approaches, including DOPRI, for different benchmarks, including classification, density estimation, and dynamical system modeling. Overall, we report a state-of-the-art performance for all benchmarks in terms of accuracy metrics, while enhancing the computational efficiency through trainable fixed-step-size solvers. This work opens up new possibilities for practical and efficient modeling applications with NODEs.

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.

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.

When considering the data-driven identification of non-linear differential equations, the choice of the integration scheme to use is far from being trivial and may dramatically impact the identification problem. In this work, we discuss this aspect and propose a novel architecture that jointly learns Neural Ordinary Differential Equations (NODEs) as well as the corresponding integration schemes that would minimize the forecast of a given sequence of observations. We demonstrate its relevance with numerical experiments on non-linear dynamics, including chaotic systems.

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.

The use of deep learning (DL) in Earth observation technology has become essential. Even though DL models do remarkably well in classifying high-resolution satellite images and extracting semantic information, they frequently need a lot of training data, which can be costly and time-consuming to obtain. With an emphasis on ocean synthetic aperture radar (SAR) image analysis, this research investigates the use of synthetically generated data using generative adversarial networks (GANs) for data augmentation. Relying on GAN-based generated images for remote sensing applications requires a thorough assessment of the quality and authenticity of the generated images, as well as validation of the model’s performance on real-world data. We assess the diversity and reliability of GAN-generated images by training a classification network on these images and evaluating their performance on real-world data. By comparing the classification accuracy in different experimental setups, we approximate the precision and recall for GANs performance.

Until recently, intensity modulations in synthetic aperture radar (SAR) altimetry waveform tails have been considered a nuisance for geophysical-parameter retrieval. These modulations are actually predictable and might be exploited using a spectral analysis of the waveform tails. After Altiparmaki et al. (2022), a more elaborated analysis is performed to improve the interpretation of these SAR altimeter spectra. A fast numerical model is developed to explain the modulation mechanisms in focused SAR altimetry waveform tails. Using numerical solutions, standard analytical closed-form solutions, are demonstrated to be invalid to retrieve ocean-wave-spectra retrievals from nadir altimeters. Although not valid, a closed-form derivation provides intuitive insights about the information contained in a SAR altimetry cross-spectrum. Under moderate environmental conditions (significant wave heights of ~2 m), a closed-form solution might still be useful to infer swell-wave spectra from swath-altimetry SAR spectra at incident angles of ~4°. Comparable to side-looking SAR ocean processing, the cross-spectral analysis for nadir signals reduces noise and might remove the 180-degree ambiguity of the wave direction. Since the synthetic aperture length of nadir altimeters is larger than sidelooking imaging SARs (e.g. Sentinel-1, RadarSat, Gaofen-3), sublook processing can be performed to compute multiple cross-spectra for the same scene. With a slightly changing observation geometry, the cross-spectra reveal slightly different parts of the ocean-wave spectrum. The resulting stack of cross-spectra can thus be used to improve the retrieval of ocean-wave parameters. Retrieved ocean-wave parameters shall then enhance the sampling of the global wave field, but also serve to advance more consistent sea-state-bias corrections.

Earth Explorer 10 mission Harmony will consist of two satellites that fly in formation with Sentinel-1. It will operate as a multistatic radar in which Sentinel-1 transmits signals and all three satellites receive signals from different lines-of-sight. To prepare for Harmony and other possible future bistatic missions, transforms are derived to map the ocean-wave spectrum into bistatic synthetic aperture radar (SAR) spectra. The SAR mapping follows the standard derivation using the multi-dimensional characteristic function, but with adjustments for the modulation transfer functions compared to the monostatic case. This paper focuses on the SAR modulations caused by velocity bunching as it is the dominant distortion mechanism. We argue that a multistatic system, such as Harmony, leads to an inversion that constrains the real aperture radar (RAR) response on a scene-by-scene basis. A benefit of having additional receivers for wave-spectra estimation is that the three lines-of-sight enables to capture a larger fraction of the wave spectrum. Improvements are especially expected in high wind speed conditions such as tropical cyclones, where large energetic surface motions strongly deteriorate the (azimuth) resolution of the SAR data. Enhanced directional wave spectral characteristics will further help to improve the interpretation of the new bistatic Harmony high-resolution scatter and Doppler combined directional measurements.

The Poseidon-4 radar altimeter on board the Sentinel-6 “Michael Freilich” (S6-MF) satellite offers unique opportunities to assess the impact of ocean surface motion on delay-Doppler altimetry (DDA). In this article, earlier “frozen-sea” studies of the instrument response to an isolated sea surface facet are extended to include the effect of surface motions in its delay-Doppler Map (DDM) signature. Integrating this elementary signature over the instrument field of view, an analytical DDA stacked echo waveform model is then derived. This waveform is validated against a well-established waveform model for the special case of a frozen sea. Model sensitivity to changes in surface significant wave height, vertical velocity standard deviation, and the geophysical Doppler vector$\mathbf {U_{\mathrm { GD}}}$projection along the satellite ground-track velocity are discussed. These developments provide theoretical and analytical means to jointly exploit the S6-MF conventional and delay-Doppler radar waveforms to improve estimates of Essential Climate Variables (sea level, sea state) and to retrieve and map two new observables (the along-track projection of the$\mathbf {U_{\mathrm { GD}}}$vector and the ocean waves vertical velocity standard deviation). These new variables, being sensitive to higher order spectral moments of the wave directional spectrum, may help to mitigate sea state range bias impacts on altimeter sea level measurements.

A new approach is developed to estimate atmospheric microwave radiation parameters (atmospheric radiation$T_{a}$and its optical thickness$\tau $) over the Arctic sea ice (SI) at the frequencies of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The approach is based on the results of numerical modeling of the brightness temperature (BT) of vertically and horizontally polarized microwave radiation of the Arctic SI–atmosphere system under nonscattering conditions. The main point of the approach is the retrieval of$\tau $at 89 GHz ($\tau _{89}$) from BT polarization difference (PD) at 89 GHz under the assumption of constant SI emission PD at 89 GHz.$T_{a}$and$\tau $at the other AMSR2 frequencies and$T_{a}$at 89 GHz are estimated using$\tau _{89}$and atmospheric water vapor column (WVC), based on the results of numerical modeling and neural networks (NNs)-based inversion algorithms. Since the retrievals of WVC can be done over SI from the AMSR2 data with the previously developed algorithm, the suggested approach allows$T_{a}$and$\tau $retrieval at the AMSR2 channel frequencies using only AMSR2 measurements without any additional data. The method is restricted by the use of the “absorption only” form of the radiative transfer equation (RTE) and is not applicable in the months from April to October when cloud and precipitation scattering start to play an important role in microwave radiation transfer. The method allows estimating the SI effective emissivities from BT measurements by means of the atmospheric correction without any reanalysis or operational analysis data. It may be used both in SI surface property studies and in the determination of the tie points in the SI concentration (SIC) retrieval algorithms. The uncertainties of$T_{a}$and$\tau $estimation greatly depend on the error of the assumption of constant SI emission PD at 89 GHz.

Synthetic Aperture Radar (SAR) image generation using Generative Adversarial Networks (GANs) has gained significant attention in recent years. In addition, the ocean plays a crucial role in regulating Earth’s climate system. SAR images provide valuable information for ocean observation and analysis, aiding in the understanding of oceanic processes and their role in climate change. This study presents a GAN-based approach for generating realistic and diverse ocean pattern SAR images. The proposed methodology combines a style-based generator network with an adversarial discriminator network to learn and reproduce the complex and unique patterns present in SAR images. In order to avoid discriminator overfitting, which frequently occurs as a result of insufficient training data, an adaptive discriminator augmentation (ADA) mechanism has been exploited. By training the GAN with ADA, the generator learns to capture the spatial and statistical properties of oceanic phenomena in restricted data regimes. The experimental results demonstrate the effectiveness and potential of the proposed GAN-based approach for ocean pattern SAR image generation, opening new avenues for advancing ocean observation and analysis in the context of climate change mitigation.

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 space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation anda prioriterms with a trainable gradient-based solver. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.

The Doppler shift obtained from synthetic aperture radar (SAR) measurements comprises the combined contribution to the radial motion of the ocean surface induced by the sea state (wind waves and swell) and underlying surface currents. Hence, to obtain reliable estimates of the ocean surface current, the sea-state-induced Doppler shifts must be accurately estimated and eliminated. In this study, we use a semiempirical dual co-polarization Doppler velocity (DPDop) model, presented in the companion paper, to calculate sea-state-induced Doppler shifts using buoy-measured wind speed, wind direction, and ocean wave spectra. The DPDop model-simulated Doppler shifts are compared with the collocated Sentinel-1B SAR Wave (WV) mode observations at the 24° and 37° incidence angles, showing a bias of -0.24 Hz and a root-mean-square error (RMSE) of 5.55 Hz. This evaluation is also implemented on a simplified DPDop model at the same incidence angles. The model inputs include wind fields from the European Center for Medium-Range Weather Forecasts (ECMWF) and wave characteristic parameters (e.g., significant wave height, mean wave direction, and mean wavenumber) from WAVEWATCH III (WW3). The estimated Doppler shifts are validated using the ascending and descending observations of Sentinel-1B WV over the global ocean. Furthermore, the comparisons show that the bias and RMSE are -0.71 Hz and 9.25 Hz, respectively. Based on accurate wave bias correction, we obtain the radial current speeds of the ocean surface from the Doppler shift measurements. The estimated current speeds are compared with the collocated high-frequency radar measurements, with a bias of -0.04 m/s and an RMSE of 0.15 m/s. These results suggest that the original and simplified DPDop models can be used to estimate sea-state-induced Doppler shifts and, thus, derive accurate surface current retrievals.

This article proposes a Doppler velocity (DV) model based on dual co-polarized (co-pol) decomposition of a normalized radar cross section of an ocean surface on polarized Bragg scattering and nonpolarized (NP) radar returns from breaking wave components. The dual co-pol decomposition provides a quantitative description of resonant and NP scattering, as well as their dependence on the incident angle, azimuth, and wind speed. Subsequently, the contributions of the facet (resonant Bragg waves and breakers) velocities, tilt, and hydrodynamic modulations due to long waves to the resulting DV can be quantified. The tilt modulation contributions to DV are estimated using the measured/empirical tilt modulation transfer function (MTF). The hydrodynamic modulations are mostly dominated by wave breaking and are estimated using a semiempirical model based on in situ measurements. In addition to the VV and HH radar data, which are required for dual co-pol decomposition and tilt MTF estimates, the surface wave spectrum is required in the DV determination for a given radar observation geometry. In this article, qualitative and quantitative consistencies are presented between the model simulations and the empirical CDOP model. In a companion paper, a DV analysis is presented to analyze the Sentinel-1 synthetic aperture radar measurements and collocated in situ measurements of surface wind and wave spectra.

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications. Considering the huge amount of data daily acquired by satellites, automated techniques for physical features extraction are needed. Even if supervised deep learning methods attain state-of-the-art results, they require a great amount of labelled data, which are difficult and excessively expensive to acquire for ocean SAR imagery. To this end, we use the subaperture decomposition (SD) algorithm to enhance the unsupervised learning retrieval on the ocean surface, empowering ocean researchers to search into large ocean databases. We empirically prove that SD improves the retrieval precision with over 20% for an unsupervised transformer auto-encoder network. Moreover, we show that SD brings an important performance boost when Doppler centroid images are used as input data, leading the way to new unsupervised physics guided retrieval algorithms.

The availability of wide swath Significant wave heights (SWH) such as those retrieved in the frame of CFOSAT and HY2B satellite missions opens important perspectives for the improvement of operational wave forecasting. The objective of this work is to analyze the impact of the combined assimilation of wide swath SWH and directional wave spectra on the integrated wave parameters in the analysis and forecast periods. The results show a significant improvement of the SWH estimate in different ocean regions. We clearly showed the persistency of the assimilation up to 3 days in the forecast period. Most striking is the ability of the combined assimilation to effectively correct swell tracking for storm events, such as Hurricane Pablo in 2019.

International audience

Spaceborne synthetic aperture radar (SAR) can provide meters-scale images of the ocean surface roughness day-or-night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel-l SAR wave mode (WV) vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition (SD) as a preprocessing stage for SAR deep learning models. Our data-centring approach surpassed the baseline by 0.7%, obtaining state-of-the-art on the TenGeoP-SARwv data set. In addition, we empirically showed that SD could bring additional information over the original vignette, by rising the number of clusters for an unsupervised segmentation method. Overall, we encourage the development of data-centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.

Characterizing the uncertainties in buoy ocean wave records is critical not only for understanding the limitations ofin situwave measurements, but also for interpreting the implied accuracies of the remotely sensed products in which these buoy data are used as validation references. This letter preliminarily assesses the error of long-period swell heights (Hss) representing specific directional wave partition energy observed from deep-water buoys moored in the northeast Pacific. We propose a buoyHsserror estimation method by combining dual and triple collocation using data derived from buoys, two kinds of space-borne radars and numerical simulations. Compared to traditional methods, the proposed approach can reveal “absolute” errors (with respect to the underlying truth) from buoyHss, accepting and then confirming that swell heights from buoy, satellite and model are all uncertain. This study simultaneously employs ocean swell products derived from synthetic/real aperture radars (Sentinel-1A/B and CFOSAT/SWIM) and WaveWatch III® ocean wave model hindcasts to diagnose the accuracy of theHssvalues observed by buoys of National Data Buoy Center (NDBC) and Coastal Data Information Program (CDIP) during the period from July 2019 to October 2021. We quantify that the NDBC’s 3-m heave-pitch-roll buoy (CDIP’s Waverider buoy) recordedHsshave root-mean-square error of 0.17 m (0.12 m), or have about 10.65% (7.06%) uncertainty relative to the meanHssvalue (approximately 1.6 m). Our findings imply that the reference value uncertainties should be taken into account when understanding direct satelliteHssvalidation against buoyin situ.

Observations of wind and ocean surface velocity vectors by along-track interferometry (ATI) with the synthetic aperture radar (SAR) are not only important for direct applications but also to increase understandings of ocean upper layer mixing, air–ocean interactions, and mapping submesoscale (1–10 km) structures. An experimental bidirectional (BiDi) ATI acquisition mode of TanDEM-X observes with two squinted beams separated by an angle of approximately 13.2° in azimuth on the ground. The baseline is very short, and the along-track interferometric phase (ATI phase) of the ocean surface in the line-of-sight direction of the beams can be interpreted as a total Doppler velocity. The 2-D Doppler velocity field will thus include wind-wave detected motions. In this article, Doppler velocity fields acquired from this experimental acquisition mode are presented. The sequential retrieval of wind vector and total surface current vectors (TSCV) is demonstrated on the BiDi TanDEM-X data. The retrieval algorithm builds on existing geophysical model functions (GMFs) of normalized radar cross section (NRCS) and Doppler velocity. XMOD2 and a GMF based on the Elfouhaily ocean wave spectrum coupled with a Kirchhoff approximation (EOWS&KA) are used. The retrieved wind fields are generally consistent with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5. While the ATI phase errors are small, the retrieved TSCV field looks promising. Acquisitions were located at sea over the tip of the Novaya Zemlya in Russia and over an area near Tromsø, Norway.

The surface wave investigation and monitoring (SWIM) aboard the China-France Oceanography Satellite (CFOSAT), a pioneer conically scanning wave spectrometer, was successfully launched on October 29, 2018. Its innovative configuration composed of one nadir and five rotating near-nadir beams is designed to simultaneously observe the directional wave spectrum at a global scale. In this study, we systematically implement the spectral analysis of the radar backscattering with the periodogram technique to obtain the fluctuation spectrum for each azimuth direction. The 2-D fluctuation spectrum of the three spectral beams ($\theta = 6^\circ $, 8°, and 10°) combines all the azimuth directions within one entire rotation of 360°. The case study demonstrates that the wave features (peak wavelength and direction) are roughly consistent between the estimated fluctuation spectrum and the collocated WaveWatch III wave slope spectrum. A marked up-to-downwave asymmetry of the fluctuation spectrum with larger spectral level in the upwave direction for all the three spectral beams is observed. A ratio is defined between the fluctuation spectrum within the [0°, 180°] sector relative to the [180°, 360°] sector. Statistics display that this ratio is greater than 1 when it denotes the up-to-downwave ratio and smaller than 1 for the down-to-upwave ratio. This observed spectrum asymmetry is linked to the asymmetric modulation from upwind to downwind. In addition, we employ such finding to help remove the 180° wave direction ambiguity from a practical point of view. Preliminary results of the direction ambiguity removal display a bias of 41.3°, 40.6°, and 36.7° for the beams. The 10° beam shows slightly better performance compared to the other two beams in terms of bias and standard deviation. This shall lay a strong basis for the operational implementation of such algorithm to resolve the direction ambiguity.

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.

In this study we use numerical modeling of the atmosphere-ocean system microwave radiation to estimate the atmospheric upwelling and downwelling radiation along with its optical thickness at the frequencies of the Advanced Scanning Microwave Radiometer 2 (AMSR2) using the values of the total atmospheric water vapor content (WVC) and total cloud liquid water content (CLW). Numerical modeling is based on the solution of the radiation transfer equation under approximations of microwave range. Calculations are done for the database of the atmospheric parameter profiles complemented with the model profiles of cloud liquid water content. The results of model calculations allow parameterizing the atmospheric radiation parameters as functions of WVC and CLW, which can be retrieved from the AMSR2 measurement data under various conditions including Tropical Cyclones (TCs). These atmospheric parameters can be further used to derive the oceanic response to high winds, associated with TCs, from the AMSR2 data.

In this study we demonstrate high potential of synergistic satellite data usage to investigate the Arctic saline sea ice. A simple geometrical optics model is explored to show the possibility to derive the First Year sea ice parameters best fitting the experimental data. Satellite Sentinel-1 Synthetic Aperture Radar (SAR) data are used to analyze the mesoscale sea ice structure and detect the age of the sea ice samples. Sentinel-3 altimeter data along with the Metop Advanced scatterometer (ASCAT) measurements of the normalized radar cross section (NRCS) are jointly used to build the NRCS dependencies on the incidence angle for the selected sea ice samples. Sea ice drift fields are used to investigate only those regions where the drift is negligible on a single day scale. It is shown that the suggested approach presents a powerful tool to study the Arctic sea ice properties.

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.

Using a combination of multi-directional SAR and TIR measurements, the Harmony Earth Explorer 10 mission candidate will provide high resolution simultaneous measurements of surface stress, surface currents SST and wave spectra over oceans, 3-D deformation vectors over solid Earth, and time-series of surface elevation changes over volcanic areas and land ice masses. This will serve a series of science objectives aimed at better understating multi-scale processed and feedbacks in the Earth System.

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.

The reconstruction of sea surface geophysical variables typically relies either on Optimal Interpolation (OI) or on model-based approaches which explicitly exploit a dynamical model. While the optimal interpolation suffers from smoothing issues making it unreliable in retrieving fine scale variability, the selection and parametrization of a dynamical model, when considering model based data assimilation strategies, remains a complex issue since several trade-offs between the models complexity and its applicability in sea surface data assimilation need to be carefully addressed. In this work, and motivated by the success of artificial intelligence algorithms in various signal processing fields as well as the increasing amounts of observations and simulation datasets, we explore a data driven Koopman model and formulate the reconstruction problem as solution of the classical Kalman filter in a space of observables. The proposed architecture although linear, is shown to outperform state-of-the-art non linear data-driven filtering schemes such as the Analog Data Assimilation (AnDA).

Based on energy and momentum conservation laws, a new simple 2D parametric model using ray tracing analysis is used to assess high sea states from tropical cyclones (TCs). From self-similar solutions of model equations, different combinations of TC parameters (maximum wind speed, TC radius and translation velocity) are performed and simplified analytical expressions are suggested for robust first-guess estimates of maximal wave length and height. A 2D geophysical model function (TC-wave GMF) is then constructed to map 2D fields for the dominant wave energy, wavelength and direction within the TC area. Results from the full model and simplified expressions are compared to airborne scanning radar altimeter measurements during hurricane Bonnie (1998) and WW3 estimates. Excellent agreement is found for both field measurements and wave spectral numerical model. While the direct model calculations are more accurate and detailed, TC-wave GMF provides useful immediate estimates of wave fields for practical nowcasting and scientific applications.

Field Ka-band radar measurements of breaking wind waves, identified with synchronized video recordings, are presented. Separating video frames with and without breakers, the breaker-free (background) and breaker-disturbed surface normalized radar cross-section (NRCS) are estimated, and analyzed along long wave profiles. Breaker NRCS signals depict clear non-polarized features near long wave crests. Simultaneous breaker radar footprint fraction measurements enables the precise estimation of pure breaker NRCS, suitable to model both wave breaking and Doppler signatures.

An advanced method for sea ice concentration (SIC) retrieval, based on measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2), is validated. An advancement relates to tie point choice for the polarization difference in satellite measurements over open water. The approach is based on the results of physical modeling of the sea ice - ocean - atmosphere system and analysis of the AMSR2 measurements in the Arctic region. The validation is done for summer months using MODIS Sea Ice Extent data as a source of ground-truth information. Areas of 100% open water and 100% sea ice are compared for the MODIS Sea Ice Extent and the AMSR2 retrieved SIC. It is found that the AMSR2 algorithm systematically underestimates 100% SIC as compared with the MODIS 100% SIC in summer months.

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.

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 forecasting and assimilation of sea surface dynamics from satellite-derived data is a challenging issue. Data-driven approaches have arisen as promising schemes to fully exploit satellite observations. A key feature of sea surface dynamics is that they relate to partially-observed dynamics. Here, guided by physical and mathematical considerations of the underlying dynamics of a given system, we propose a novel neural networks architecture for the identification of ordinary differential equations (ODE) of partially observed systems. Numerical experiments for toy models and a sea level anomaly dynamics illustrate the relevance of the proposed scheme for forecasting and assimilation issues w.r.t. other state-of-the-art data-driven models.

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.

Satellite altimeters, designed to measure the topography of the ocean, are also good imagers of the ocean surface backscatter. A method was developed to invert the classical Low Resolution Mode high resolution HR waveforms in terms of surface backscatter at high resolution (~300m). Since the launch of Cryosat-2 in 2010, a new generation of altimeters using Doppler capabilities has emerged and has become the standard for the new altimeters such as the Sentinel-3 one. The so-called SAR altimetry provides higher along-track resolution and lower noise level. In this mode altimeter are also good imagers of the surface backscatter. A new method of inversion of SAR waveforms is presented. The processing of the Sentinel-3 altimeters provides collocated SAR and LRM waveforms that are used to intercompare the inverted surfce backscatter.

An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI-open ocean-atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014-2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements.

In the last years, Neural Networks have enriched the state-of-the-art in probabilistic modeling. This is principally due to the advances in deep learning which allow a better understanding of complex systems. However, the stochastic representation of spatio-temporal fields is still an open challenge that may benefit from the recent advances in probabilistic modelization. In this work, we explore neural network to derive a stochastic representation of spatio-temporal dynamical systems based on ensemble forecasting. Trough the implementation of our stochastic model in a classical Kalman filtering scheme, we demonstrate the relevance of the proposed architecture in the reconstruction of geophysical fields with respect to the state-of-the-art approaches.

The China-France Oceangraphy SATellite (CFOSAT) is an innovative satellite mission with wind and waves measurements on oceans. This paper aims to evaluate the first results of the assimilation of the wave data provided by CFOSAT in the wave model MFWAM. Model runs are implemented during the Calibration/Validation phase of the mission. The results are compared to the wave data from altimeters and buoys. The first results are promising and indicate a significant improvement of wave heights in the different ocean basins (high latitudes, intermediate latitudes and the tropics). Azimuthal cut-off sensitivity tests for the SWIM wave spectra are also examined in this study. We also discussed the impact of combined wave spectra and the ones from the incidence angles of SWIM (6, 8 and 10°). In other respects this study also investigate the complementary use of SWIM and SAR from CFOSAT and SAR from Sentinel-1 for combined assimilation in the wave model MFWAM

This work describes the Arctic Portal - a geographic information system (GIS), allowing monitoring and forecasting the state and dynamics of the ocean-atmosphere-cryosphere system in the Arctic region, based on the integrated use of satellite and model data. The paper summarizes the general features of the Arctic Portal and clarifies its main structural peculiarities. The Arctic Portal is a freely available web application, which uses a set of programming technologies and open source libraries, providing reliable and fast satellite data processing, the final goal of which is to visualize data on the Arctic map. In addition to data representation, it has several capabilities, which makes usage of the Portal the "first step" for educational or scientific research.

This work is aimed at the investigation of the application of general machine learning (ML) algorithms, used typically for image recognition, to ocean surface type categorizing, using Synthetic Aperture Radar (SAR) images as input data. The study is focused on the Convolutional Neural Network (CNN) based image segmentation. Its main goal is building the fully automatic pipeline, calculating (predicting) ocean surface (particularly ice) type from a SAR image.It is shown that the developed system has classification accuracy of up to 90% without any preliminary feature selection or region-based limitations. The advantages and drawbacks of the suggested approach are discussed.Sea ice - sea water discrimination and sea ice concentration estimation from Sentinel-1 SAR images are considered in more details due to their general availability and extensive coverage of the Arctic region.

This work deals with data-driven and learning-based approaches to fill space-time sampling gaps in the observation of sea surface tracers such as Sea Surface Height (SSH), Sea Surface temperature (SST), Ocean Colour,... More precisely, we jointly address field super-resolution and the prediction of subgrid-scale features, which is novel to our knowledge. From a methodological point of view, we consider deep learning architectures with a view to learning geophysically-sound differential operators (i.e. trasnport operators). Based on an Observing System Simulation Experiment representative of SWOT fast sampling phase using NATL60 simulation data, we illustrate the relevance of the proposed methodological framework which reconstructs more than 90% of the variance of the high-resolution SSH anomaly field and above 90% of the subgrid-scale variance of this anomaly. We also illustrate significant gain w.r.t. other baseline neural network architectures and further discuss the relevance of the reported contribution for other tracer fields and case studies.

RADARSAT-2 C-band quad-polarization ocean synthetic aperture radar (SAR) scenes are decomposed into resonant Bragg scattering from regular (no-breaking) surface and scattering from breaking waves. Analysis of the surface current signatures in dual co- and cross-pol SAR images revealed that governing imaging mechanism is modulations of wave breakings which are very sensitive to the presence of current non-uniformities. As found, due to small relaxation scale, short Bragg waves do not "feel" the current. Thus routinely observed current signatures in quad-pol SAR images originate essentially from wave breaking modulations, and modulation of Bragg waves does not matter this issue.

This work describes the operational geographic information system - Arctic portal - developed at the Satellite Oceanography Laboratory of the Russian State Hydrometeorological University and a new, created with this instrument, database of the Polar lows (PL) and less intensive mesocyclones (MC) observed over the entire Arctic region for the period of 2017-2018. The database is created by the synergistic use of satellite data of spectral and microwave radiometers and scatterometers implemented in the Arctic portal allowing most reliable PL and MC identification. This database presents a complicated structure of PL and MC parameters for the whole period of their lifetime. The database is of particular interest as it covers not only the Northern seas, but also the Eastern sector of the Russian Arctic, where PL events are poorly investigated, and where the formation of the MCs has become possible due to recent sea ice retreat.

This study provides quantitative estimates of wind waves breaking contribution to dual co- and cross-polarized radar scattering. Extending approaches suggested by [1], [2], 1696 RADARSAT-2 quad-polarized Synthetic Aperture Radar measurements collocated with 65 in situ National Data Buoy Center buoys observations are analyzed. For VV polarization, wave breaking contribution decreases from 60% to 20% with increasing incidence angle, while for HH polarization and cross-polarization wave breaking contribution is about 60%-70% at all incidence angles. Empirical dependencies of wave breaking contributions to co- and cross-pol signals on wind speed, incidence angles and azimuth are then suggested.

This paper provides a compact overview of Harmony, an Earth Explorer 10 mission candidate dedicated to the observation of dynamic deformations of ice, solid earth and ocean surfaces. Harmony consists of two receive-only small Synthetic Aperture Radar (SAR) satellites using Sentinel-1D as illuminator, which will alternate close formation phases, dedicated to single-pass cross-track interferometry, with StereoSAR phases dedicated to the study of ocean surface motion and 3-D land surface deformations.

The "Sea surface KInematics Multiscale monitoring" (SKIM) concept is a satellite mission designed to measure the world ocean surface currents, ice drift and ocean waves at spatial scales of 40 km [1] . It has been recently selected by the European Space Agency (ESA) as a finalist of the Earth Explorer 9 (EE9) call of proposal alongside with the FORUM concept. The onboard sensor will be a coherent rotating Ka-band radar providing Doppler measurements at 6 and 12 degrees incidence. This configuration allows for measurement of the directional wave spectrum, which yields accurate corrections of the wave-induced bias in the current measurements. Near nadir Doppler Ka-band measurements over the ocean have shown sensibilities to sea surface kinematics [2] , [3] and the surfacic motions due to waves and current are the main geophysical contributors to the measured microwave Doppler shift. However, these measured velocities have complex relations to oceanic motion and theoretical as well as numerical inspections are necessary to fully understand the undelying physical interactions. The understanding of this relation is at the heart of the SKIM mission measurement concept whose main objective is to provide the best estimate of the underlying sea surface current. The purpose of this work is to show how the strongest contribution of the geophysical Doppler shift is due to waves motion (the so called wave bais) and how it can be estimated to finally extract the current contribution. A theroretical approach is presented and compared to experimental results obtained from airborne campain (not presented in this abstract). To complete this two approaches, results of complex numerical simulations, processed on the High Performance Computer of Ifremer, involving a maximum of physical phenomena (satellite, sensor and surface kinematics, complex ocean electromagnetic scattering, ...) as well as onboard processings (range migration, pulse-pair algorithm, ...) are presented to corroborate theoretical predictions and experimental results.

In this work, we propose an alternative to the Ensemble Kalman filter through the implementation of a neural networks filtering scheme based on a parametric stochastic model. From our numerical experiment, we prove the relevance of the proposed architecture in the reconstruction of geophysical fields with respect to the state-of-the-art schemes.

Recent studies have investigated the identification of governing equations of geophysical systems from data. Here, we investigate such identification issues for ocean surface dy-namcis from ocean remote sensing data. From a methodological point of view, we address the learning of data-driven dynamical models when only provided with a noisy training dataset. We propose a novel architecture that relies on data assimilation schemes to learn the underlying dynamical model through the minimization of a reconstruction cost. We demonstrate the relevance of the proposed architecture with respect to the state-of-the-art approaches in the identification and forecasting of synthetic and real case-studies.

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.

New empirical geophysical model functions (GMFs) have been developed to interpret 89-GHz brightness temperature measurements over cold Arctic seas. Careful data screening is applied to the Advanced Microwave Scanning Radiometer 2 (AMSR2) multifrequency measurements to exclude atmospheric scattering and large absorption impacts, to estimate the 89-GHz sea surface emissivity and its relative changes with surface wind speed (SWS). Matched-up wind speeds are directly derived from the AMSR2 low-frequency measurements. The GMFs are obtained from the AMSR2 level 1R 89 GHz measurements corrected for the atmospheric impact with physical models and atmospheric parameters derived from the AMSR2 data. A carefully selected database encompasses mostly cold sea conditions (<;4 °C) and a large range of wind speed conditions, including extratropical cyclone and polar low high-wind events over the Nordic Seas. Resulting GMFs contrast with previously proposed ones manifesting larger SWS signal at horizontal and positive SWS signal at vertical polarization.

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.

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.

The increasing number of directional wave spectra from satellites is a very important opportunity for the improvement of operational wave forecast. This will lead to a better understanding of swell dissipation which is still a top task in wave modeling. This work aims to investigate how the assimilation of different sources of directional wave spectra will impact the analysis and forecast of wave parameters. Two satellites, sentinel-1A and 1B, provide the level 2 SAR wave spectra, which have been qualified for operational use regarding to the calibration/validation phase developed in the last two years. This year the launch of the satellite CFOSAT will ensure a third source directional wave spectra from the real aperture radar SWIM and a significant wave height at nadir look. Synthetic wave data of SWIM and wave spectra from S1A and S1B are assimilated conjointly in the wave model MFWAM for 1 cycle of 13 days during the southern winter season. Two sets of synthetic SWIM data with and without instrumental errors have been tested in the assimilation. The validation of the results with altimeters significant wave heights shows a significant impact in the periods of analysis and forecast. In this study we also investigated the impact of instrumental errors provided by SWIM simulator and the wavelength cut-off on the assimilation results.

A comprehensive description of the Sea State Bias (SSB) for wide-swath interferometric (WSI) altimetry is proposed using a two-scale transfer function of the first-of-its-kind instrument, the KaRIn instrument of the SWOT mission. We show that the SSB encompass the contribution of the ElectroMagnetic Bias (EMB) similarly to conventionnal altimetry. The new contributions in the SSB balance originate from the slant-range sampling of the waves and their orbital velocities. It involves cross-correlation between heights, radar cross section and orders of orbital velocities. We discuss the implication of each of the cross-correlation in the total SSB.

Polar lows are the most intense subclass of mesocyclones developing at high latitudes above ice-free sea surface. These dangerous phenomena are capable of generating high wind waves even when cyclone is stationary. Moreover, as in case of tropical cyclones, a trapped-fetch effect may occur, i.e., when a polar low translates, waves in its right sector remain under high wind forcing for a prolonged time and grow to anomalous heights. Here we provide a model evaluation of the probability of appearance of such anomalous waves using a recently suggested wave enhancement criterion for moving extreme events and a new polar low climatology. As found, a significant part of the observed polar lows is capable of generating anomalously high waves. Frequency of appearance of abnormally high waves with heights exceeding given level (e.g. 6, 8, 10m) in the area of interest is presented.

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 GlobCurrent project (http://www.globcurrent.org) aimed to: (i) advance the quantitative estimation of ocean surface currents from satellite sensor synergy; and (ii) demonstrate impact in user-led scientific, operational and commercial applications that, in turn, would improve and strengthen the uptake of satellite measurements. It is often demonstrated that sharp gradients in the sea surface temperature (SST) and current fields and the ocean surface chlorophyll-a distribution are spatially correlated with the sea surface roughness anomaly fields at small spatial scales, in the sub-mesocale (1-10 km) to the mesoscale (30-80 km). The 2-dimensional structures manifested in the satellite observations represent evidence of the upper ocean dynamics. Whereas the quasi geostrophic assumption is valid for the upper ocean dynamics at the larger scale (> 100 km), possible triggering mechanisms for the expressions at the mesoscale-to-submesoscale may include spiraling tracers of inertial motion and the interaction of the wind-driven Ekman layer with the quasi-geostrophic current field. This latter, in turn, produces bands of downwelling (convergence) and upwelling (divergence) near fronts. A regular utilization of the sensor synergy approach with the combination of Sentinel-3, Sentinel-2 and Sentinel-l together with other satellite missions will provide a highly valuable data set for further research and development to better relate the 2-dimensional surface expressions and the upper ocean dynamics.

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.

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.

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.

The reconstruction of ocean surface wave characteristics, i.e. directional energy distribution and dispersion, from ocean sunglitter images is discussed. Wave spectral analysis is performed, through the definition of linear transfer functions, to relate image brightness variations and sea surface slopes. Surface-current Doppler shift modifies the wave dispersion relation and can be obtained from cross-correlating two images and tracking glint changes over a known time. The technique is tested on photographs, taken from an aircraft in the Gulf of Mexico, and from a drone in the Black sea. The method is further extended to satellite sunglitter imagery, taking advantage of the technical peculiarities of Sentinel- 2 sensors helping to infer both the brightness gradient in the along-track direction. Wave dispersion information through the inter-channel time delay allows to reconstruct the current vector. The results demonstrate encouraging prospects of using the sunglitter imagery for the sea state monitoring, including formation of abnormally high waves and the study of wave-current interactions at different scales.

Ka-band Radar Doppler velocity is compared to the optical velocity of deep water breaking wind waves observed in field conditions from a stationary oceanographic platform. One typical record is analyzed: wind speed is 11 m/s, dominant wave frequency is 0.24 Hz, radar incidence angle is 53°, and radar-to-wind azimuth is upwind. This 30 minute record includes 42 breaking wave events. The Doppler velocity is estimated as the first moment of instantaneous Doppler spectrum. The optical velocity of breakers is measured using spatially collocated and temporally synchronized video records of the radar footprint. As found the Doppler velocity of breakers is remarkably slower (by a factor of 4) than the “optical” velocity of whitecaps. This suggests that whitecaps (translated with phase velocities of breaking waves) represent a moving source of the enhanced surface roughness providing radar returns. Since this roughness is “embedded” into the water, its Doppler velocity is much smaller than the phase velocity.

In this study Era-Interim re-analysis data on the atmospheric total water vapor content (WVC), total cloud liquid water content (CWC), ice water content (IWC) and profiles of the atmospheric meteorological parameters for the whole 2015 year are used to analyze WVC, IWC and CWC seasonal variability over the sea ice and sea water surfaces and estimate the influence of the atmospheric integrated water parameters on the total microwave radiation of the sea ice and sea water - atmosphere system at the frequencies and polarizations of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The brightness temperature (BT) of the sea ice - atmosphere system microwave radiation for the AMSR2 channel characteristics is modeled for the conditions of non-scattering atmosphere. The arrays of BTs are calculated using published data on the sea ice emissivities and Era-Interim re-analysis data on the sea ice concentration (SIC) and temperature.

The performance of weather filters in satellite passive microwave sea ice concentration retrieval algorithms is analyzed under conditions of high winds. The analysis is based both on numerical modeling results and on the measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) over high wind areas. It is shown that when the threshold values for the gradient ratios are used to classify measurements as being taken over open water, sea surface wind speed geophysical model function (GMF) uncertainty plays an important role in such weather filtering. The GMF uncertainty at higher AMSR2 frequencies is mostly pronounced at high winds. The gradient ratios are calculated using the radiative transfer modeling for the conditions of non-precipitating atmosphere and an ensemble of atmospheric meteorological profiles combined with ocean parameter data. The threshold values for the gradient ratios are set up basing on numerical calculations.

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.

Multisatellite measurements of altimeter-derived sea surface height (SSH) have provided a wealth of information on the ocean. Yet, horizontal scales below 100 km remain scarcely resolved. Especially, in the Mediterranean Sea, an important fraction of the mesoscale range, characterized by a small Rossby radius of deformation of 15-20 km, is not properly retrieved by altimeter-derived gridded products. Here, we investigate a novel processing of AVISO products with a view to resolving the horizontal scales sensed by current along-track altimeter data. The key feature of our framework is the use of linear convolutional operators to model the fine-scale SSH detail as a function of different sea surface fields, especially optimally interpolated SSH and sea surface temperature (SST). The proposed model embeds the surface quasi-geostrophic SST-SSH synergy as a special case. Using an observing system simulation experiment with simulated SSH data from model outputs in the Western Mediterranean Sea, we show that the proposed approach has the potential for improving current optimal interpolations of gridded altimeter-derived SSH fields by more than 20% in terms of relative SSH and kinetic energy mean square error, as well as in terms of spectral signatures for horizontal scales ranging from 30 to 100 km. Our results also suggest that SST-SSH relationship may only play a secondary role compared with the interscale SSH cascade. We further discuss the relevance of the proposed approach in the context of future altimetric satellite missions.

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.

Radar scattering from ocean surfaces is investigated by 3-D numerical solution of Maxwell's equations [numerical Maxwell's model in 3-D (NMM3D)] using the ocean surface profiles stochastically generated from a 3-D Durden-Vesecky ocean spectrum. The surface integral equations (SIEs) are formulated for dielectric surfaces using Green's functions of the air and the ocean permittivities with the surface tangential electric and magnetic fields as the unknowns. In solving the SIEs using the method of moment, a fast matrix solver of the sparse matrix canonical grid is used in conjunction with Rao-Wilton-Glisson basis functions. The computation has been implemented on a high-performance parallel computing cluster for problems with up to six million surface unknowns. Unlike the two-scale model (TSM) approximation, NMM3D does not require division of the surface spectrum into large- and small-scale ocean waves. The results of backscattering simulations are compared to Aquarius satellite radar measurements for wind speeds of 5, 8, and 10 m/s and for incidence angles of 29°, 39°, and 46°. The results show that NMM3D ocean backscattering solutions at L-band are in good agreement with Aquarius satellite radar data for co-polarized VV, HH, and cross-polarized VH returns as well as for the VV/HH ratio. The azimuthal dependence of L-band backscatter is also assessed. Finally, NMM3D results are compared to TSM solutions and are shown to lie close to Aquarius data in observed VV/HH ratio, and their azimuthal dependencies.

This paper presents dual copolarized (VV and HH) Ka-band radar measurements of joint modulation of normalized radar cross section (NRCS) and Doppler velocity (DV) performed from a sea research platform. NRCS and DV modulations are well correlated. NRCS modulations exhibit a spiky structure. HH modulations are stronger than VV ones leading to modulations of the polarization ratio. This suggests that an important portion of NRCS modulations is produced by nonpolarized radar returns from modulated wave breaking facets. DV modulations reveal that at incidence angles <;50°, NRCS spikes are attributable to rather slow moving facets, which may be interpreted as short wave breaking disturbances embedded in the water at crests of modulating waves. Using the DV as a proxy for wave gauge, a modulation transfer function (MTF) is estimated for both polarizations. The hydrodynamics component of the total MTF, hydro-MTF, combines NRCS modulations supported by Bragg waves and wave breaking. The contribution of each type of facets to the hydro-MTF is weighted by its partial contribution to the NRCS, and thus, hydro-MTF becomes dependent on radar polarization. Using hydro-MTF for HH and VV, Bragg wave and wave breaking modulations are separated. Wave breaking modulations are significant, with MTF amplitude varying from 5 to about 30 depending on wind speed. Ka-band Bragg waves are strongly modulated at low winds, but their modulation almost vanishes at moderate winds. Finally, we propose an empirical MTF parameterization based on polynomial fitting as a function of observation geometry and wind.

We address in this paper the reconstruction of irregurlarly-sampled image time series with an emphasis on geophysical remote sensing data. We develop a data-driven approach, referred to as an analog assimilation and stated as an ensemble Kalman method. Contrary to model-driven assimilation models, we do not exploit a physically-derived dynamic prior but we build a data-driven dynamic prior from a representative dataset of the considered image dynamics. Our contribution is here to extend analog assimilation to images, which involve high-dimensional state space. We combine patch-based representations to a multiscale PCA-constrained decomposition. Numerical experiments for the interpolation of missing data in satellite-derived ocean remote sensing images demonstrate the relevance of the proposed scheme. It outperforms the classical optimal interpolation with a relative RMSE gain of about 50% for the considered case study.

This paper addresses the reconstruction of high-resolution Sea Surface Height (SSH) from the synergy between along-track altimeter data, OI-interpolated SSH fields and satellite-derived high-resolution Sea Surface Temperature (SST) fields. We aim at better resolving the fine-scale range, typically below 100km, which remains scarcely resolved by operational optimal interpolation schemes. The proposed scheme relies on multi-tracer convolutional models and on their calibration from the observed along-track data. We explore a dictionary-based decomposition of the convolutional models to improve the robustness of the calibration. We report a numerical evaluation using an Observation Simulation System Experiment (OSSE) for a case study region in the western Mediterranean sea. Our numerical experiments demonstrate that we can improve reconstruction performance by about 20%, in terms of mean square error, compared to optimally-interpolated fields. Dictionary-based decompositions also resort to similar potential improvement. We further analyze different parameterizations of the convolution models in relation to physical priors (e.g., SGQ dynamics, isotropical transfer functions).

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.

The generalized curvature ocean surface scattering model [general curvature model (GCM)] is extended and revisited. Two key steps are addressed in this paper, namely, a necessary sea surface spectrum undressing procedure and the inclusion of a skewness phase-related component. Normalized radar cross-section (NRCS) simulations are generated at C-band for various wind conditions, polarizations, and incidence angles. Results are compared with CMOD5.n. Although the sea surface spectrum undressing procedure is a necessary step, the overall NRCS dynamic is notably affected only in low wind conditions (≤5 m/s). The inclusion of the skewness phase-related component makes the most impact to the NRCS dynamic where the upwind/downwind asymmetry is clearly detectable. A good agreement between the upwind/downwind asymmetry of the extended GCM and CMOD5.n is achieved for moderate winds (≈5-10 m/s) and moderate incidence angles (≈32°-40°). For low incidence angles (<;26°), the GCM tends to overestimate the upwind/downwind asymmetry compared with CMOD5.n.

This paper presents dual copolarized (PP) (VV and HH) Ka-band sea surface backscattering measurements taken from the Black Sea research platform at incidence angles ranging from 25° to 65° and in the wind speed range from 3 to 18 m/s. These measurements are corrected for radar antenna pattern and geometry of observations. The resulting normalized radar cross section (NRCS) is parameterized in a form of truncated azimuthal Fourier series with coefficients dependent on the incidence angle and wind speed. This dual PP empirical model (KaDPMod) is consistent with the Ku-band NSCAT-4 model. However, some remarkable differences are revealed. They are apparent when analyzed using a decomposition of VV and HH measurements into polarized Bragg backscattering (polarization difference, PD = VV-HH) and nonpolarized (NP) backscattering from breaking waves. The polarization difference (PD) has strong azimuth and wind dependencies, with the wind exponent ranging from 2.5 to 3. The saturation wave spectra derived from multifrequency PD (based on KaDPMod and Ku- and C-band empirical models) have a notable peak in the capillary-gravity range. The relative contribution of NP radar return to the Ka-band NRCS is significant. In the upwind direction, it reaches up to 60%-80% and 25%-50% for HH and VV, respectively. It is found that the NP wind exponent is lower than that for Bragg backscattering. Therefore, the relative contribution of the NP to Ka-band NRCS decreases with increasing wind speed at both polarizations. Such a behavior is the opposite of that observed in the Ku-band.

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.

GEROS-ISS (GEROS hereafter) stands for GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. It is a scientific experiment, proposed to the European Space Agency (ESA) in 2011 for installation aboard the ISS. The main focus of GEROS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) for remote sensing of the System Earth with focus to Climate Change characterisation. The GEROS mission idea and the current status are briefly reviewed.

Polar lows (PLs), emerging over the sea ice edge, are studied using multisensor data information, surface analysis maps and reanalysis data. PLs over the Western (the Greenland, the Norwegian and the Barents Seas), and the Eastern (the Chukchi, the East Siberian and the Laptev Seas) parts of the Arctic are considered. It is shown that currently operating satellite instruments, taken separately, cannot provide means for PL confident detection. Whereas multisensor satellite data, including passive and active microwave data products along with infrared and visible measurements, analyzed simultaneously, provide an opportunity to monitor PLs, developed under most environmental conditions, including the vicinity to the sea ice edge. Geophysical parameters are retrieved from satellite passive microwave data with advanced algorithms. PLs are detected using the complex geo-informational system, making advantage from simultaneous analysis of different satellite and model data.

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.

With the sea-ice decline over the eastern part of the Eurasian Arctic (EEA), polar mesocyclones (MCs) and their most intensive representatives-polar lows (PLs)-can occur over more open-water areas. Visible and infrared MODIS images, active and passive microwave spaceborne instruments, and ERA Interim reanalysis data are combined and used to analyze the synoptic situations and to infer the factors influencing MC appearance and evolution over the Kara Sea, the Laptev Sea, the East Siberian Sea, and the Chukchi Sea. In recent years, the Arctic more often loses its summer sea-ice cover, and PLs may more commonly emerge within open-water Eastern Arctic regions during fall and summer months. This conclusion is derived basing on the analysis of more than 150 MCs for August, September, October, and November months over the EEA for the period of 2003-2015. PL and MC distribution and characteristics necessitate to be studied with a multisensor approach since neither reanalysis data nor data from a single instrument can provide sufficient means for their confident detection and analysis.

Measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission-Water 1 (GCOM-W1) satellite at 6.925 and 7.3 GHz and both linear polarizations over tropical cyclones (TCs) during 2012-2014 are used to derive a new geophysical function relating the brightness temperature to the sea surface wind speed (SWS) in extreme conditions. Similar sensitivity to the SWS at close C-band frequencies allowed correcting for the atmospheric contributions to the microwave radiance and estimating the brightness temperature (TB) at the surface under TCs, combining theoretical modeling and measured TBanalyses. Estimated oceanic TB's were regressed against the wind speeds from the Best Track Archive to derive a new geophysical model function for the wind speed excess emissivity at AMSR2 C-band microwave frequencies.

In this work taking the advantage of high resolution spaceborne synthetic aperture radar (SAR) measurements we present first preliminary results of short-period internal waves (IW) observations in the Barents, Kara and White seas based on ENVISAT ASAR data for summer-autumn months in 2007–2011. Altogether more than 2000 IW packets were identified in about 1400 SAR images. Detailed maps of internal wave's occurrences in the three Arctic seas have helped to identify main sites of regular IW generation.

In this study we exploit the polarization sensitivity of high-resolution SAR measurements in C- and L-bands using a methodology suggested in [1, 2] to study the physics of radar imaging of upper ocean dynamics. Using samples of RADARSAT-2, ALOS PALSAR and ALOS-2 PALSAR-2 quad-polarization ocean SAR scenes, we decompose dual co-polarized (VV and HH) radar data into polarization difference (PD) and non-polarized (NP) components. Following [1, 2] these quantities provide means to distinguish Bragg scattering and non-polarized radar returns related to the quasi-specular reflection from breaking waves and from slopes of “regular” large-scale waves, and assess their relative role in the formation of surface current signatures in C- and L-band SAR ocean images. While C-band SAR features of ocean currents are fully dominated by non-resonant and non-polarized radar scattering from breaking waves, their manifestation in L-band SAR images are formed by Bragg waves, wave breaking and large-scale “regular” waves modulations by the currents.

Synoptic maps of total ocean surface currents from space are needed to improve the characterisation and parameterisations of oceanic sub-mesoscale dynamics and represent their impact on global ocean circulation, air-sea exchanges and the marine ecosystem. Wavemill is a satellite mission concept based on a dual beam along-track interferometric SAR principle. An airborne proof-of-concept campaign took place in the tidally-dominated Irish Sea in 2011. A comprehensive collection of airborne flights arranged in a star pattern permitted to sample various azimuthal directions. Here, it is shown that the impact of ocean waves on the measured ocean surface motion can be as large or even larger than the strong tidal current (0.7m/s). Using in situ validation measurements and airborne Wavemill data, the azimuth dependence and magnitude of this wind-wave velocity artefact is characterised.

A new method for radio-frequency interference (RFI) contamination identification over open oceans for the two C-subbands and X-band of Advanced Microwave Scanning Radiometer 2 (AMSR2) channel measurements is suggested. The method is based both on the AMSR2 brightness temperature (TB) modeling and on the analysis of AMSR2 measurements over oceans. The joint analysis of TBspectral differences allowed to identify the relations between them and the limits of their variability, which are ensured by the changes in the environmental conditions. It was found that the constraints, based on the ratio of spectral differences, are more regionally and seasonally independent than the spectral differences themselves. Although not all possible RFI combinations are considered, the developed simple criteria can be used to detect most RFI-contaminated pixels over the World Ocean for AMSR2 measurements in two C-subbands and the X-band.

We introduce a practical and accurate model, referred to as “GO4,” to describe near-nadir microwave scattering from the sea surface, and at the same time, we address the issue of the filtered mean square slope (mss) conventionally used in the geometrical optics model. GO4 is a simple correction of this last model, taking into account the diffraction correction induced by the rough surface through what we call an effective mean square curvature (msc). We evaluate the effective msc as a function of the surface wavenumber spectrum and the radar frequency and show that GO4 reaches the same accuracy as the physical optics model in a wide range of incidence and frequency bands with the sole knowledge of the mss and msc parameters. The key point is that the mss entering in GO4 is not the filtered but the total slope. We provide estimation of the effective msc on the basis of classical sea spectrum models. We also evaluate the effective msc from near-nadir satellite data in various bands and show that it is consistent with model predictions. Non-Gaussian effects are discussed and shown to be incorporated in the effective msc. We give some applications of the method, namely, the estimation of the total sea surface mss and the recalibration of relative radar cross sections.

A Doppler centroid Dcmodel based on the generalized curvature ocean surface scattering model (generalized curvature model or GCM) is presented. Two key features are included in this model: a skewness-related phase coefficient based on empirical skewness coefficients of sea-surface-slope probability density function (pdf) for wind speed less than 10 m/s and effects from wave breaking for wind speed greater than 10 m/s. Simulated Dcvalues are exclusively compared with the empirical geophysical Doppler model function named CDOP, for hh and vv polarizations, various wind conditions, and incidence angles. Good agreement is found overall between CDOP and simulated Dc values. The overall bias for simulated Dc-hhwith and without skewness are 2.63 versus -0.51 Hz (14.6 versus -2.8 cm/s), respectively; overall standard deviations are 2.76 versus 3.53 Hz (15.3 versus 19.6 cm/s). For simulated Dc-vv,overall bias values with and without skewness are -0.16 versus -2.52 Hz (-0.9 versus -14 cm/s); standard deviations are 3.56 versus 4.32 Hz (19.7 versus 24 cm/s). The overall bias for simulated Dc-hhwith and without the wave breaking component are -0.08 versus 0.12 Hz (-0.4 versus 0.7 cm/s), respectively; corresponding standard deviations are 3.32 versus 4.75 Hz (18.4 versus 26.3 cm/s). Bias values for simulated Dc-vvwith and without the wave breaking component are -1.83 versus -2.02 Hz (-10.2 versus -11.2 cm/s), with corresponding overall standard deviations of 3.43 versus 4.87 Hz (19 versus 27 cm/s). The largest deviation from CDOP, of about 18 Hz (0.99 m/s), is found in the upwind direction for a 26° incidence angle, 10-m/s wind speed, and hh polarization.

This paper addresses the simulation of high-resolution geophysical fields from low-resolution satellite observations in the context of the remote sensing of the ocean surface. Within a super-resolution framework, we investigate texture-based stochastic models while controlling high-resolution spectral, geometrical and statistical features. We introduce a novel model stated as the solution of a SPDE (Stochastic Partial Differential Equation) associated with conditional 2D Gaussian field. We address both model parameter inference from real high-resolution images and simulation issues. Experiments for Sea Surface Temperature fields demonstrate the relevance of our model compared to classical stationary schemes.

This paper addresses the reconstruction of high-resolution (HR) sea surface wind fields (typically, at a spatial resolution of 1 km). The availability of such HR fields is critical for numerous issues, e.g. coastal management, offshore structures, oil spill disaster tracking, etc. Satellites, especially from Synthetic Aperture Radar (SAR) systems, can monitor the ocean surface at a spatial resolution of a few meters. SAR wind fields are operationally produced with spatial resolutions of less than 1 km [1, 2]. However, satellite SAR systems involve a highly irregular sampling of the ocean surface and, for a given region, SAR wind fields may be delivered with a low temporal resolution, typically every 7-to-10 days for temperate zones. By contrast, model predictions, such as European Center for Medium-range Weather Forecast (ECMWF) wind fields, are typically delivered with a high temporal resolution (e.g. every 3 h or 6 h), but with a low spatial resolution (~50km × 50km). The question of the combination of numerical model predictions and SAR wind fields naturally arises to deliver HR wind fields at sea surface anywhere and anytime. Here, we state this issue as the statistical learning of transfer functions between low-resolution (LR) model predictions and the associated HR SAR fields. We investigate the extent to which such regression functions can be learnt from a set of co-located HR and LR fields. Both local and non-local schemes as well as linear and non-linear regression methods are considered. As a case-study, we carry out numerical experiments for a coastal area off Norway, which involves complex LR-to-HR situations.

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.

The statistical properties of the electromagnetic field backscattered from sea surfaces are studied by using asymptotic numerical methods. Sea surfaces are modeled by using the Elfouhaily et al. spectrum model. The influence of the radar spatial resolution on the field statistics is studied for various wind conditions and radar configurations, by considering equal range and azimuth resolutions. It is observed that the backscattered field phase can be assimilated to a uniform distribution and that the amplitude resembles a Rayleigh distribution. Moreover, reducing the radar spatial resolution d induces a stronger variability of the backscattered field amplitude and a departure from the Rayleigh distribution. This departure is enhanced particularly when the radar look approaches the cross-wind direction and also when increasing the wind speed. Also, a fitting of the amplitude distribution with various theoretical statistical laws (gamma, Weibull, K, and so on) highlights the general good fitting of the K distribution, which is in agreement with previous work led from measurements.

Multisatellite measurements of altimeter-derived sea surface height (SSH) and sea surface temperature (SST) provide a wealth of information about ocean circulation, particularly mesoscale ocean dynamics which may involve strong spatiotemporal relationships between SSH and SST fields. Within an observation-driven framework, we investigate the extent to which mesoscale ocean dynamics may be decomposed into a mixture of dynamical modes, characterized by different local regressions between SSH and SST fields. Formally, we develop a novel latent class regression model to identify dynamical modes from joint SSH and SST observation series. Applied to the highly dynamical Agulhas region, we demonstrate and discuss the geophysical relevance of the proposed mixture model to achieve a spatiotemporal segmentation of the upper ocean dynamics.

This paper addresses stochastic geometry-driven image models and its application to super-resolution issues. Whereas most stochastic image models rely on some priors on the distribution of grey-level configurations (e.g., patch-based models, Markov priors, multiplicative cascades,...), we here focus on geometric priors. We aim at simulating texture samples while controlling high-resolution geometrical features. In this respect, we introduce a stochastic model for texture orientation fields stated as a 2D Orstein-Uhlenbeck process. We show that this process resorts in the stationary case to priors on orientation statistics. We exploit this model to state image super-resolution as a geometry-driven variational minimization, where the geometry is sampled from the proposed conditional 2D Orstein-Uhlenbeck process. We demonstrate the relevance of this approach for real images associated with the remote sensing of ocean surface dynamics.

The use of satellite Sea Surface Temperature (SST) fields to retrieve zonal and meridional surface currents (U, V) is now a widespread idea. Since the classical approach involves temporal differencing of SST fields, we investigate in this paper the extent to which mesoscale ocean dynamics may be decomposed into a superposition of dynamical modes, characterized by different linear relationships between surface currents and temperature fields. Based on a completely observation-driven approach, we propose a latent class regression model from local satellite surface currents and patches of SST measurements. Applied to the highly dynamical Agulhas region, we demonstrate and discuss the geophysical relevance of the proposed mixture model to achieve a spatio-temporal segmentation and tracking of the ocean surface dynamical modes. Moreover, we show the accuracy of the proposed model to predict mesoscale surface currents from SST single maps.

An effective methodology using satellite high-resolution polarized information to interpret and quantitatively assess various surface ocean phenomena is suggested. Using a sample RADARSAT-2 quad-polarization ocean synthetic aperture radar (SAR) scene, the dual co-polarization (VV and HH) radar data are combined into polarization difference, polarization ratio, and nonpolarized components. As demonstrated, these field quantities provide means to distinguish Bragg scattering mechanism and radar returns from breaking waves. As shown, quantitative characteristics of the surface manifestation of ocean currents, slicks, and wind field features in these dual co-polarization properties are very different and may be effectively used in the development of new SAR detection and discrimination algorithms.

To evaluate the proposed model we used SST and SSS observations. High resolution SST observations are taken from the METOP dataset and corresponding low resolution observations from the REMSS database. High and low resolution SSS observations are taken from the ESA/SMOS (European Space Agency/Soil Moisture and Ocean Salinity) database.

In this paper, for moderate winds, the statistics of the field backscattered from sea surfaces are studied by simulation, by using a numerical model. It is observed that the field phase and amplitude are close to a uniform distribution and a Rayleigh distribution, respectively. However, reducing the radar resolution limit induces a departure from the Rayleigh distribution. This departure depends on several parameters: it gets stronger when the wind speed increases or the incidence angle decreases.

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.

In this letter, we evaluate to which extent the activity of ocean fronts can be retrieved from the geometric regularity of ocean tracer observations. Applied to sea surface temperature (SST), we propose a method for the characterization of this geometric regularity from curvature-based statistics along temperature level lines in front regions. To assess the effectiveness of the proposed descriptors, we used six years (from 2003 to 2008) of daily SST observations of the regions of Agulhas in the South of Africa and of Malvinas off the southern Brazilian coast. These experiments stress the relevance of geometric regularity features of tracer observation at ocean surface to characterize seasonal variations in ocean regimes.

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.

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.

Multi-angular images of the brightness temperature (TB) of the Earth at 1.4 GHz are reconstructed from the Soil Moisture and Ocean Salinity (SMOS) satellite sensor data since end 2009. Sea surface salinity (SSS) products remote sensing from space is being attempted using these data over the world oceans. The quality of the first version of the European Space Agency operational Level 2 (L2) SSS swath products is assessed in this paper, using satellite/in situ SSS data match-ups that were collected over the second half of 2010. This database reveals that 95% of the SMOS L2 products show a global error standard deviation on the order of ~ 1.3 practical salinity scale. Simple spatiotemporal aggregation of the L2 products to generate monthly SSS maps at 1° ×1° spatial resolution reduces the error down to about 0.6 globally and 0.4 in the tropics for 90% of the data. Several major problems are, however, detected in the products. Systematically, SMOS SSS data are biased within a ~ 1500 km wide belt along the world coasts and sea ice edges, with a contamination intensity and spread varying from ascending to descending passes. Numerous world ocean areas are permanently or intermittently contaminated by radio-frequency interferences, particularly in the northern high latitudes and following Asia coastlines. Moreover, temporal drifts in the retrieved SSS fields are found with varying signatures in ascending and descending passes. In descending passes, a time-dependent strong latitudinal bias is found, with maximum amplitude reached at the end of the year. Errors in the forward modeling of the wind-induced emissivity and of the sea surface scattered galactic sources are as well identified, biasing the sss retrievals at high and low winds and when the galactic equator sources are reflected toward the sensor.

The processing steps and error corrections needed to retrieve estimates of sea surface range Doppler velocities from Envisat Advanced Synthetic Aperture Radar Wide Swath Medium resolution image products are presented. Retrieval accuracies based on examination of the corrected Doppler shift measurements are assessed. The root-mean-square errors of the Doppler shift after bias corrections are found to be 4.7 and 3.9 Hz in VV and HH polarizations, respectively. At 35° incidence angle, this corresponds to horizontal Doppler velocities of 23 and 19 cm/s.

This paper presents static and dynamic variational data assimilation methods for missing data interpolation in sea surface temperatures (SST) images. Evaluation using 50 AVHRR METOP SST images assesses the effectiveness of the proposed methods.

Monitoring sea surface temperature (SST) is of high interest. The dynamics of climatic events such as tropical storms or cyclones are closely related to the sea surface temperature. An important research topic in oceanography is about the understanding of the interactions between the ocean's surface and it's deeper layers. In Lapeyre et al. show that in the case of baroclinic unstable flows, the potential vorticity of mesoscale and submesoscale structures in the ocean interior are strongly correlated to the surface density structures. Thus, in frontal regions, knowledge of the ocean surface structures regularity gives insights about the 3D dynamics of the ocean. In this paper we focus on the analysis of the SST images level lines regularity from satellites images in regions in the neighborhood of oceanic fronts. Two regions of interest are considered: the region of Benguela and the region of Malvinas. Our investigations suggest that SST fronts belong to the class of statistical self similar curves. Taking into account self similar curves properties, we propose local curvature based descriptors for SST front region regularity characterization. We experimentally assess the efficiency of our descriptors by measuring their ability to capture SST image regularity seasonal variations.

To progress in the understanding of the impact of nonlinear wave profiles in scattering from sea surfaces, a nonlinear model for infinite-depth gravity waves is considered. This model, termed as the “Choppy Wave” Model (CWM), is based on horizontal deformation of a linear reference random surface. It is numerically efficient and enjoys explicit second-order statistics for height and slope, which makes it well adapted to a large family of scattering models. We incorporate the CWM into a Kirchhoff or small-slope approximation and derive statistical expressions for the corresponding incoherent cross section. We insist on the importance of “undressing” the wavenumber spectrum to generate a nonlinear surface with a prescribed spectrum. Interestingly, the inclusion of nonlinearities is found to be practically compensated by the spectral undressing process; an effect which might be specific to the CWM and needs to be investigated in the framework of fully nonlinear models. Accordingly, the difference between the respective normalized radar cross section is rather small. The most noticeable changes are faster azimuthal variations and a slight increase of the radar returns at nadir. A statistical analysis of sea clutter in the framework of a two-scale model is also performed at large but nongrazing incidence. It shows a pronounced polarization dependence of the distribution of large backscattered amplitudes, the tail being much larger in horizontal polarization and for small resolution cell. Surface nonlinearities are shown to increase the tail of the amplitude distribution, as expected. Less obviously, their relative impact is found lesser in horizontal polarization. This raises the question of the actual contribution of nonlinearities in radar sea spikes at nongrazing angles.

We examine how the rough sea surface scattering of L-band celestial sky radiation might affect the measurements of the future European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. For this purpose, we combined data from several surveys to build a comprehensive all-sky L-band celestial sky brightness temperature map for the SMOS mission that includes the continuum radiation and the hydrogen line emission rescaled for the SMOS bandwidth. We also constructed a separate map of strong and very localized sources that may exhibit L-band brightness temperatures exceeding 1000 K. Scattering by the roughened ocean surface of radiation from even the strongest localized sources is found to reduce the contributions from these localized strong sources to negligible levels, and rough surface scattering solutions may be obtained with a map much coarser than the original continuum maps. In rough ocean surface conditions, the contribution of the scattered celestial noise to the reconstructed brightness temperatures is not significantly modified by the synthetic antenna weighting function, which makes integration over the synthetic beam unnecessary. The contamination of the reconstructed brightness temperatures by celestial noise exhibits a strong annual cycle with the largest contamination occurring in the descending swaths in September and October, when the specular projection of the field of view is aligned with the Galactic equator. Ocean surface roughness may alter the contamination by over 0.1 K in 30% of the SMOS measurements. Given this potentially large impact of surface roughness, an operational method is proposed to account for it in the SMOS level 2 sea surface salinity algorithm.

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.

This letter uses a large ocean satellite data set to document relationships between Ku-band radar backscatter (sigmao) of the sea surface, near-surface wind speed (U), and ocean wave height (SWH). The observations come from satellite crossovers of the Tropical Rainfall Mapping Mission (TRMM) Precipitation Radar (PR) and two satellite altimeters, namely: 1) Jason-1 and 2) ENVISAT. At these nodes, we obtain TRMM clear-air normalized radar cross-section data along with coincident altimeter-derived significant wave height. Wind speed estimates come from the European Centre for Medium-Range Weather Forecast. TRMM PR is the first satellite to measure low incidence Ku-band ocean backscatter at a continuum of incidence angles from 0deg to 18deg. This letter utilizes these global ocean data to assess hypotheses developed in past theoretical and field studies.

Since the sun is an extremely strong radiation source at L-band, accounting for sun glint over the ocean, i.e., solar radiation reflected by the sea surface toward downward-looking radiometers, raises a significant challenge for the remote sensing of sea surface salinity. This paper describes a dedicated physical model for sun glint at L-band frequencies and provides quantitative and qualitative estimates of the sun glint contamination impinging the antenna of the Microwave Imaging Radiometer with Aperture Synthesis interferometer onboard the future European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. The sun brightness temperature expected during the mission period is first estimated from past solar flux data with an expected range of to about . Numerical simulations of the predicted illumination of the SMOS antenna by solar radiation scattered by the rough sea surface are then performed at key dates of the seasonal cycle using different asymptotic scattering models and several representative surface conditions. Although the center of the sun's glitter pattern will never be located within the useful part of SMOS' synthesized field of view, the expected contamination due to roughness scattering will range between 0 K and about 500 K, depending on the target position, the season period, the roughness state at the target, and the level of solar activity at the time of measurements. In particular, we find the sun glint contamination to be more intense when SMOS will probe ocean surfaces in the Southern Hemisphere, reaching maxima in descending passes with highest values expected at dates around winter solstices.

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.

Of the satellite radiometer sensors, there has been only one instrument that provides any heritage at L-band: the Skylab S-194 instrument that operated in the 1970s. From an analysis of S-194 brightness temperature (Tb) sensitivity to SSS, SST and wind speed, Lerner & Hollinger (1976) concluded that the wind speed dependence of L-band brightness temperature at nadir is about 0.16 K/knot. This is almost four times higher than what is predicted by recently developed sea surface emissivity models at L-band and twice the experimental value reported during the Bering Sea Experiments. To investigate the possible reasons for such discrepancies, two data sets acquired by the S-194 Skylab instrument from 1973-1974 missions are used in the present paper in conjunction with products from climate model reanalysis projects as ancillary data. The re-analyses shows that it is very likely that Lerner & Hollinger overpredicted the quasi-linear wind speed dependence of L-band sea surface emissivity at nadir by a factor of about 2, main discrepancies being due to different wind speed data used in the analysis. Still, we found that emissivity models for the foam free sea surface based on the small perturbation method underestimate the roughness impact at nadir by a factor of 2. Including the foam impact cannot explain all the differences.

Sea surface salinity can be measured by microwave radiometry at L-band (1400-1427 MHz). This frequency is a compromise between sensitivity to the salinity, small atmospheric perturbation, and reasonable pixel resolution. The description of the ocean emission depends on two main factors: (1) the sea water permittivity, which is a function of salinity, temperature, and frequency, and (2) the sea surface state, which depends on the wind-induced wave spectrum, swell, and rain-induced roughness spectrum, and by the foam coverage and its emissivity. This study presents a simplified two-layer emission model for foam-covered water and the results of a controlled experiment to measure the foam emissivity as a function of salinity, foam thickness, incidence angle, and polarization. Experimental results are presented, and then compared to the two-layer foam emission model with the measured foam parameters used as input model parameters. At 37 psu salt water the foam-induced emissivity increase is /spl sim/0.007 per millimeter of foam thickness (extrapolated to nadir), increasing with increasing incidence angles at vertical polarization, and decreasing with increasing incidence angles at horizontal polarization.

The study reports an assessment of global ocean wave model (Wavewatch III) outputs using altimeter algorithms wave statistics at global and regional scales. The focus is upon the sensitivity if the modeled wave moments to two distinct types of wind forcing fields, one from the NCEP atmospheric model analysis and the other from a blended product combining NCEP with scatterometer winds (QuickSCAT).

In order to improve the wave forecast, the assimilation of directional wave spectra in wave model has been developed at Meteo France. The assimilation scheme is tested for real ENVISAT ASAR wave spectra provided by the European Space Agency. The scheme takes into account the limitation in frequency related to the ASAR instrument. The preliminary results show that the assimilation of wave spectra is efficient and works correctly. The impact of the assimilation on wave parameters is significant and stays effective in the forecast period. In addition, statistical analysis has shown good skill in term of root mean square and assimilation index for the analyzed wave parameters (wave height, direction and frequency).

EM scattering from ocean surface are described using a generalized curvature expansion of the fields at an elevated non-perfect conducting surface. The new theory describes in general the scattering of EM waves from an undulated ocean surface, and reconstructs perfectly the case of Bragg scattering (Wright, 1966), and the case of perfect conducting surface (Elfouhaily et al., 1999). The formalism is applied to the case of backscattering, and expression from normalized radar cross section for different polarization combinations is given.

One month (March 2003) of ASAR wave mode level 2 data are validated using collocated with ECMWF WAM spectra and wind speed data. The geophysical validation of wind and wave parameters shows good agreement in terms of mean wave periods, T/sup p/, T/sup p//sub 12/, but the ASAR tends to measure a longer mean swell period (/spl ap/0.7s). for the ASAR level 2 significant waveheight, H/sup s/, saturation effects at high sea-states are observed. Less saturation effects observed when considering only waves with periods above 12s, H/sup p//sub 12/. Good correlation is also observed for the mean spectral wave direction, /spl Pi//sub 12/, for the long wave part of the spectrum. The saturation in H/sup s/ is mostly due to the well-known effect of azimuth roll-off, which increases with increasing wind sea states. Statistics and global maps comparison are also presented, showing the capabilities of ASAR wave mode to provide global coverage of wave spectral parameters.

The small slope approximation (SSA) theory is applied to the prediction of the foam-free sea surface brightness temperatures at L-band and nadir incidence angle. If surface geometry is assumed to be Gaussian, the wind induced polarized brightness temperature signals are integral functions of the product of the directional curvature spectrum harmonics and electromagnetic weighting functions. At nadir incidence angle, these electromagnetic weighting functions are equals in magnitude for horizontal and vertical polarization and exhibit peaky distribution as function of wavenumber. Using these properties together with azimuthal characteristics of the directional curvature spectrum at L-band, the combination of vertically and horizontally polarized brightness temperature signals at normal incidence is shown to form a simple linear equation of the dielectric constant, the SST and the wind direction. A simple analytical inversion algorithm is proposed to retrieve the Sea Surface Salinity without a priori knowledge of the wind speed using the previously established dependence.

Elfouhaily et al. (2003) generalized Woodward's theorem and applied it to the case of random signals jointly modulated in amplitude and frequency. This generalization yields a new spectral technique to estimate the amount of energy due to mode coupling without calling for higher order statistics. Two power spectra are detected; the first is related to the independent modes, and the second contains extra energy caused by mode coupling. This detection is now extended from frequency to time-frequency domain. A comparison between a wavelet transform and our time-frequency technique shows good agreement along with new insight into the time occurrence of the nonlinearities or mode coupling. An application to water surface waves is given as an example.

Global brightness temperature observations of the TOPEX/Poseidon microwave radiometer (TMR) at 18, 21, and 37 GHz have been collocated with near-simultaneous SeaWinds wind vector data as well as with monthly sea surface temperature and salinity products. The combined data allow us to study the dependence of zenith-directed ocean surface emissivity, at each frequency, upon both wind speed and direction. Results show a clear two-branch wind speed dependence; weak and linear below 7 m s/sup -1/ with an increase in sensitivity above that point. The observed emissivity also depends on the angle between the wind direction and TMR's antenna polarization orientation, providing satellite confirmation of aircraft-derived results. There is little change in these wind vector dependencies with frequency.

Differences between sea surface wind speed estimated from the measurements of two different Ku-band satellite-borne sensors are investigated. The first sensor is the altimeter radar on board the European Remote Sensing Satellite, ERS-2, of the European Space Agency. The second one is the NASA scatterometer, NSCAT, on board the Japanese satellite ADvanced Earth Observing System (ADEOS). Because of the different physical processes involved in the measurements of the two sensors, differences are expected between the two retrieved surface wind speed data. The analyzed data set was extracted from the ERS-2 acid NSCAT collocated data base set up, within ADEOS validation activities, by the French Centre ERS d'Archivage et de Traitement (CERSAT), from the Institut Francais de Recherche pour l'Exploitation de la Mer. The dataset covers almost the whole life of NSCAT, from September, 1996, to June, 1997, and consists in more than 800000 pairs of collocated altimeter and scatterometer measurements. On average, wind speed estimates were found to be in agreement over the medium 4-9 ms/sup -1/ wind speed range. At low speed, NSCAT wind speed is overestimated relative to the altimeter one, and inversely, at high wind speed, over 10-17 ms/sup -1/ range, NSCAT is underestimated. The impact of significant wave height and incidence angle on difference between wind speed retrievals from the two sensors was then investigated. Some systematic behaviors were observed as a function of these two parameters and discussed in view of other available analysis and backscatter modeling development.

Higher order moments have been, for the last decade, an important field of interest, but generally limited to one dimensional signal cases. We introduce in this paper a 2D bispectrum to detect nonlinearity in the SAR image mapping process, by using the bicoherency of a 2D signal which is theoretically flat over all frequencies, if the process is linear. Two bicoherency estimators are developed, the first one using a direct method for bispectral estimation and a periodogram for the spectral estimator, and the second one based on an indirect method and correlogram. In order to validate our nonlinearity detection method, we have tested it first on two simulated images, a linear one and a nonlinear one, and then on two SAR images, one seeming to be nonlinear. Conclusions are drawn by comparing the SAR image and the simulated image bicoherencies.