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The Copernicus Sentinel-6 Michael Freilich (S6-MF) and Jason-3 (J3) Tandem Experiment (S6-JTEX) provided over 12 months of closely collocated altimeter sea state measurements, acquired in "low-resolution" (LR) and synthetic aperture radar "high-resolution" (HR) modes onboard S6-MF. The consistency and uncertainties associated with these measurements of sea state are examined in a region of the eastern North Pacific. Discrepancies in mean significant wave height (Hs, 0.01 m) and root-mean-square deviation (0.06 m) between J3 and S6-MF LR are found to be small compared to differences with buoy data (0.04, 0.29 m). S6-MF HR data are found to be highly correlated with LR data (0.999) but affected by a nonlinear sea state-dependent bias. However, the bias can be explained robustly through regression modelling based on Hs. Subsequent triple collocation analysis (TCA) shows very little difference in measurement error (0.18 ± 0.03 m) for the three altimetry datasets, when analysed with buoy data (0.22 ± 0.02 m) and ERA5 reanalysis (0.27 ± 0.02 m), although statistical precision, limited by total collocations (N = 535), both obscures interpretation and motivates the use of a larger dataset. However, we identify uncertainties in the collocation methodology, with important consequences for methods such as TCA. Firstly, data from some commonly used buoys are found to be statistically questionable, possibly linked to erroneous buoy operation. Secondly, we develop a methodology based on altimetry data to show how statistically outlying data also arise due to sampling over local sea state gradients. This methodology paves the way for accurate collocation closer to the coast, bringing larger collocation sample sizes and greater statistical robustness. [ABSTRACT FROM AUTHOR]

Abstract. Coastal seas, shelf seas and marginal ice zones are dominated by small-scale ocean surface dynamic processes that play a vital role in the transport and exchange of climate-relevant properties like carbon, heat, water and nutrients between land, ocean, ice and atmosphere. Mounting evidence indicates that ocean scales below 10 km have far-ranging impacts on airsea interactions, lateral ocean dispersion, vertical stratification, ocean carbon cycling, and marine productivity -- governing exchanges across key interfaces of the Earth System, the global ocean and atmosphere circulation and climate. Yet, these processes remain poorly observed at the fine spatial and temporal scales necessary to resolve them. The Ocean Surface Current Airborne Radar (OSCAR) is a new airborne instrument with the capacity to inform these questions by mapping vectorial fields of total ocean surface currents and winds at high resolution over a wide swath. Developed for the European Space Agency ESA), OSCAR is the airborne demonstrator of the satellite mission concept 'SeaSTAR', which aims to map total surface current and ocean wind vectors with unprecedented accuracy, spatial resolution and temporal revisit across all coastal seas, shelf seas and marginal ice zones. Like SeaSTAR, OSCAR is an active microwave Synthetic Aperture Radar Along-Track Interferometer (SAR-ATI) with optimal three-azimuth sensing enabled by unique highly-squinted beams. In May 2022, OSCAR was flown over the Iroise Sea, France, in its first scientific campaign as part of the ESA-funded SEASTARex project. The campaign successfully demonstrated the capabilities of OSCAR to produce high-resolution 2D images of total surface current vectors and near-surface ocean vector winds, simultaneously, in a highly dynamic, macrotidal coastal environment. OSCAR current and wind vectors show excellent agreement against ground-based X-band radar derived surface currents, numerical model outputs and NovaSAR-1 satellite SAR imagery, with Root-Mean-Square differences against X-band radar better than 0.2 m s-1 for currents at 200 m resolution. These results are the first demonstration of simultaneous retrieval of total current and wind vectors from a high-squint three-look SAR-ATI instrument, and the first geophysical validation of the OSCAR and SeaSTAR observing principle. OSCAR presents a remarkable new ocean observing capability to support the study of small-scale ocean dynamics and air-sea interactions across the Earth's coastal, shelf and polar seas. [ABSTRACT FROM AUTHOR]

Coastal seas, shelf seas and marginal ice zones are dominated by small-scale ocean surface dynamic processes that play a vital role in the transport and exchange of climate-relevant properties like carbon, heat, water and nutrients between land, ocean, ice and atmosphere. Mounting evidence indicates that ocean scales below 10 km have far-ranging impacts on air-sea interactions, lateral ocean dispersion, vertical stratification, ocean carbon cycling, and marine productivity – governing exchanges across key interfaces of the Earth System, the global ocean and atmosphere circulation and climate. Yet, these processes remain poorly observed at the fine spatial and temporal scales necessary to resolve them. Ocean Surface Current Airborne Radar (OSCAR) is a new airborne instrument with the capacity to inform these questions by mapping vectorial fields of total ocean surface currents and winds at high resolution over a wide swath. Developed for the European Space Agency (ESA), OSCAR is the airborne demonstrator of the satellite mission concept 'SeaSTAR', which aims to map total surface current and ocean wind vectors with unprecedented accuracy, spatial resolution and temporal revisit across all coastal seas, shelf seas and marginal ice zones. Like SeaSTAR, OSCAR is an active microwave Synthetic Aperture Radar Along-Track Interferometer (SAR-ATI) with optimal three-azimuth sensing enabled by unique highly-squinted beams. In May 2022, OSCAR was flown over the Iroise Sea, France, in its first scientific campaign as part of the ESA-funded SEASTARex project. The campaign successfully demonstrated the capabilities of OSCAR to produce high-resolution 2D images of total surface current vectors and near-surface ocean vector winds, simultaneously, in a highly dynamic, macrotidal coastal environment. OSCAR current and wind vectors show excellent agreement against ground-based X-band radar derived surface currents, numerical model outputs and NovaSAR-1 satellite SAR imagery, with Root-Mean-Square differences against X-band radar better than 0.2 m s-1 for currents at 200 m resolution. These results are the first demonstration of simultaneous retrieval of total current and wind vectors from a high-squint three-look SAR-ATI instrument, and the first geophysical validation of the OSCAR and SeaSTAR observing principle. OSCAR presents a remarkable new ocean observing capability to support the study of small-scale ocean dynamics and air-sea interactions across the Earth's coastal, shelf and polar. [ABSTRACT FROM AUTHOR]

We will show that ocean-reflected signals from the global positioning system (GPS) navigation satellite constellation can be detected from a low-earth orbiting satellite and that these signals show rough correlation with independent measurements of the sea winds. We will present waveforms of ocean-reflected GPS signals that have been detected using the experiment onboard the United Kingdom's Disaster Monitoring Constellation satellite and describe the processing methods used to obtain their delay and Doppler power distributions. The GPS bistatic radar experiment has made several raw data collections, and reflected GPS signals have been found on all attempts. The down linked data from an experiment has undergone extensive processing, and ocean-scattered signals have been mapped across a wide range of delay and Doppler space revealing characteristics which are known to be related to geophysical parameters such as surface roughness and wind speed. Here we will discuss the effects of integration time, reflection incidence angle and examine several delay-Doppler signal maps. The signals detected have been found to be in general agreement with an existing model (based on geometric optics) and with limited independent measurements of sea winds; a brief comparison is presented here. These results demonstrate that the concept of using bistatically reflected global navigation satellite systems signals from low earth orbit is a viable means of ocean remote sensing. Index Terms--Bistatic radar, global navigation satellite systems (GNSS), global positioning system (GPS), delay-Doppler mapping, oceanography, QuikSCAT, reflectometry, satellite remote sensing.

Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed "Sea State" as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data , last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1 ,), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b ,) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478 ,). [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study concerning the development of a sea state bias (SSB) model designed with three parameters exclusively derived from altimeter data and globally applied to all reference altimeter missions. The proposed technique, first tested for the Jason-1 mission, proves to have a good performance for a wide range of ocean conditions when compared with the state-of-the-art SSB corrections currently in use. In addition to the significant wave height ($H_{s}$) and wind speed ($U~_{{10}}$), a third predictor acting as a mediator parameter gathered by the mean wave period ($T_{z}$) has been used. Two different empirical algorithms for altimeter ocean wave period have been tested and implemented, improving the SSB model performance in some ocean regions. The methodology relies on nonparametric modulation and statistical techniques based on smoothing splines embedded in a generalized additive model. This SSB modeling approach shows good performance when applied to all reference missions, in particular to TOPEX and Jason-2 missions, slightly reducing the explained variance of sea-level anomaly (SLA) when compared with the established SSB models. The approach is computationally efficient, capable of generating a stable SSB model using a small training data set when little information is available, as is the case with the recent Jason-3 mission. Model performance is assessed by comparison with existing SSB corrections for each reference mission, intercomparisons during the period of the tandem phases, and by SLA variance analysis, providing a consistent set of SSB corrections for the four reference missions. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive procedure to improve the wind geophysical model function (GMF) for the Global Navigation Satellite System Reflectometry (GNSS-R) instrument onboard the TechDemoSat-1 satellite. The observable used to define the GMF is extracted from the measured delay-Doppler maps (DDMs) by correcting for the nongeophysical effects within the measurements. Besides the instrument and the geometric effects as provided in the bistatic radar equation, a calibration term that accounts for the uncalibrated receiver antenna gain and the unknown transmitter antenna gain is proposed to optimize the calculation of GNSS-R observables. Such calibration term is presented as a function of observing elevation and azimuth angles and is shown to remarkably reduce the measurement uncertainties. First, an empirical wind-only GMF is developed using the collocated Advanced Scatterometer (ASCAT) winds and European Centre for Medium-Range Weather Forecasts (ECMWF) model wind output. This empirical GMF agrees well with the model output. Then, the sensitivity of the observable to waves is analyzed using the collocated ECMWF wave parameters. The results show that it is difficult to include mean square slope (MSS) in the development of an empirical GMF, since the difference between ECMWF MSS and the MSS sensed by GNSS-R varies with incidence angle and wind speed. However, it is relevant to take significant wave height ($H_{s}$) in account, particularly for low wind conditions. Consequently, a wind/ $H_{s}$ approach is proposed for improved wind retrievals. [ABSTRACT FROM AUTHOR]

Abstract: We present the first examples of Global Navigation Satellite Systems‐Reflectometry (GNSS‐R) observations of hurricanes using spaceborne data from the UK TechDemoSat‐1 (TDS‐1) mission. We confirm that GNSS‐R signals can detect ocean condition changes in very high near‐surface ocean wind associated with hurricanes. TDS‐1 GNSS‐R reflections were collocated with International Best Track Archive for Climate Stewardship (IBTrACS) hurricane data, MetOp ASCAT A/B scatterometer winds, and two reanalysis products. Clear variations of GNSS‐R reflected power (σ0) are observed as reflections travel through hurricanes, in some cases up to and through the eye wall. The GNSS‐R reflected power is tentatively inverted to estimate wind speed using the TDS‐1 baseline wind retrieval algorithm developed for low to moderate winds. Despite this, TDS‐1 GNSS‐R winds through the hurricanes show closer agreement with IBTrACS estimates than winds provided by scatterometers and reanalyses. GNSS‐R wind profiles show realistic spatial patterns and sharp gradients that are consistent with expected structures around the eye of tropical cyclones. [ABSTRACT FROM AUTHOR]

An assessment of non-geophysical effects in spaceborne global navigation satellite system reflectometry (GNSS-R) data from the UK TechDemoSat-1 (TDS-1) mission is presented. TDS-1 was launched in July 2014 and provides the first new spaceborne GNSS-R data since the pioneering UK-disaster monitoring constellation experiment in 2003. Non-geophysical factors evaluated include ambient L-band noise, instrument operating mode, and platform-related parameters. The findings are particularly relevant to users of uncalibrated GNSS-R signals for the retrieval of geophysical properties of the Earth surface. Substantial attitude adjustments of the TDS-1 platform are occasionally found to occur that introduce large uncertainties in parts of the TDS-1 GNSS-R dataset, particularly for specular points located outside the main beam of the nadir antenna where even small attitude errors can lead to large inaccuracies in the geophysical inversion. Out of eclipse however, attitude adjustments typically remain smaller than 1.5°, with larger deviations of up to 10° affecting less than 5% of the overall sun-lit data. Global maps of L1 ambient noise are presented for both automatic and programmed gain modes of the receiver, revealing persistent L-band noise hotspots along the Equator that can reach up to 2.5 dB, most likely associated with surface reflection of signals from other GNSS transmitters and constellations. Sporadic high-power noise events observed in certain regions point to sources of human origin. Relevant conclusions of this study are that platform attitude knowledge is essential and that radiometric calibration of GNSS-R signals should be used whenever possible. Care should be taken when considering using noise measurements over the equatorial oceans for calibration purposes, as ambient noise and correlated noise in delay–Doppler maps both show more variation than might be expected over these regions. [ABSTRACT FROM PUBLISHER]

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging technique that uses navigation opportunistic signals as a multistatic radar. Most GNSS systems operate at L-band, which is affected by the ionosphere. At present, there is only a GNSS-R space-borne scatterometer on board the UK TechDemoSat-1, but in late 2016, NASA will launch the CYGNSS constellation, and in 2019, ESA will carry out the GEROS experiment on board the International Space Station. In GNSS-R, reflected signals are typically processed in open loop using a short coherent integration time (∼1 ms), followed by long incoherent averaging (∼1000 times, ∼1 s) to increase the signal-to-noise ratio. In this study, the global ionospheric scintillation model is first used to evaluate the total electron content and the scintillation index S4. The ionospheric scintillation impact is then evaluated as a degradation of the signal-to-noise ratio, which can be used to assess the altimetry and scatterometry performance degradation in a generic GNSS-R mission. Since ionospheric scintillations are mostly produced by a layer of electron density irregularities at ∼350 km height, underneath most LEO satellites, but closer to them than to the Earth's surface, intensity scintillations occur especially in the GNSS transmitter-to-ground transect, therefore, the impact is very similar in conventional and interferometric GNSS-R. Using UK TechDemoSat-1 data, signal-to-noise ratio fluctuations are computed and geo-located, finding that they occur in the open ocean along ∼±20° from the geomagnetic equator where S4 exhibits a maximum, and in low wind speed regions, where reflected signals contain a non-negligible coherent component. [ABSTRACT FROM PUBLISHER]

GNSS-Reflectometry is a new technique that shows promise for many earth observation applications including remote sensing of oceans, land, and ice. A payload has been developed that is low size and power, and suitable for use on small satellites. The first flight of the SGR-ReSI GNSS Reflectometry Instrument is on the TechDemoSat-1 mission, launched in July 2014. The instrument has been operational since its commissioning in September 2014, and has been collecting delay Doppler maps routinely over many different surfaces. Preliminary work has been undertaken to develop and validate wind speed inversion algorithms against ASCAT measurements with promising results. Measurements over land and sea ice are also showing interesting geophysical characteristics This paper describes the instrument, early operations, data dissemination through the Measurement of Earth Reflected Radio-navigation Signals By Satellite (MERRByS) website and preliminary data assessments in preparation for further data exploitation. [ABSTRACT FROM PUBLISHER]

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant. [ABSTRACT FROM PUBLISHER]

A conceptually simple formulation is proposed for a new empirical sea state bias (SSB) model using information retrieved entirely from altimetric data. Nonparametric regression techniques are used, based on penalized smoothing splines adjusted to each predictor and then combined by a Generalized Additive Model. In addition to the significant wave height (SWH) and wind speed (U10), a mediator parameter designed by the mean wave period derived from radar altimetry, has proven to improve the model performance in explaining some of the SSB variability, especially in swell ocean regions with medium-high SWH and low U10. A collinear analysis of scaled sea level anomalies (SLA) variance differences shows conformity between the proposed model and the established SSB models. The new formulation aims to be a fast, reliable and flexible SSB model, in line with the well-settled SSB corrections, depending exclusively on altimetric information. The suggested method is computationally efficient and capable of generating a stable model with a small training dataset, a useful feature for forthcoming missions. [ABSTRACT FROM AUTHOR]

The backscatters power single-look waveform recorded by a synthetic aperture radar altimeter is approximated in a closed-form model. The model, being expressed in terms of parameterless functions, allows for efficient computation of the waveform and a clear understanding of how the various sea state and instrument parameters affect the waveform. [ABSTRACT FROM PUBLISHER]

A Minimum Variance (MV) wind speed estimator for Global Navigation Satellite System-Reflectometry (GNSS-R) is presented. The MV estimator is a composite of wind estimates obtained from five different observables derived from GNSS-R Delay-Doppler Maps (DDMs). Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions that are derived from NDBC buoy wind matchups with collocated overpass measurements made by the GNSS-R sensor on the United Kingdom-Disaster Monitoring Constellation (UK-DMC) satellite. The MV estimator exploits the partial decorrelation that is present between residual errors in the five individual wind retrievals. In particular, the RMS error in the MV estimator, at 1.65 m/s, is lower than that of each of the individual retrievals. Although they are derived from the same DDM, the partial decorrelation between their retrieval errors demonstrates that there is some unique information contained in them. The MV estimator is applied here to UK-DMC data, but it can be easily adapted to retrieve wind speed for forthcoming GNSS-R missions, including the UK's TechDemoSat-1 (TDS-1) and NASA's Cyclone Global Navigation Satellite System (CYGNSS). [ABSTRACT FROM PUBLISHER]

Wave-breaking dissipation is one of the least understood processes implemented in contemporary wave models. Significant effort has been put in its parameterization, but it has not proven to be totally satisfactory, either theoretically or practically. In this work, the WAVEWATCH III (version 2.22; Tolman) wave model is used to evaluate the two wind input/dissipation source term packages that it includes: (i) Wave Model (WAM) cycle 3 (WAMDIG) and (ii) Tolman and Chalikov. Global model outputs were obtained under the same wind forcing for the two dissipation formulations and were collocated in space and time in the north Indian Ocean with Ocean Topography Experiment (TOPEX) altimeter data. The performance of the model was assessed by evaluating the statistical behavior of the collocated datasets. The parameters examined were significant wave height, wind speed, wind direction, wave direction, wave height for fully developed seas, and energy loss due to wave breaking. From the results, the behavior of the input/dissipation formulations in specific wind and wave conditions was identified; that is, the results give insight to the way the two source term packages “work” and how they respond to local wind sea or swell. Specifically, both of the packages were unable to perform adequately during a season when the area can be mostly affected by swell. However, the results confirmed that the examination of only integral spectral wave parameters does not give information on the inherent physical characteristics of the formulations. Further study, on the basis of point spectra, is necessary to examine the formulations’ performance across the wave spectrum. [ABSTRACT FROM AUTHOR]

Investigates the development and validation of altimeter wind speed algorithms following the collation of coincident altimeter and buoy open ocean measurements. Significance of altimeter wind and wave measurements to global ocean circulation and climatological studies; Overview of wind error statistics over the full wind speed range for single parameter algorithms; Effect of dataset composition and measurement errors on algorithm development.

Global Navigation Satellite System Reflectometry (GNSS-R) is a rapidly developing Earth observation technology that makes use of signals of opportunity from Global Navigation Satellite Systems that have been reflected off the Earth's surface. The Cyclone Global Navigation Satellite System (CyGNSS) is a constellation of eight small satellites launched by NASA in 2016, carrying dedicated GNSS-R payloads to measure ocean surface wind speed at low latitudes (±35° North/South). The ESA ECOLOGY project evaluated CyGNSS v3.0 products, which were recently released following various calibration updates. This paper examines the performance of the new calibration by evaluating CyGNSS v3.0 Level-1 Normalised Bistatic Radar Cross Section (NBRCS) and Leading Edge Slope (LES) data from individual CyGNSS units and different GPS transmitters under constant ocean wind conditions. Results indicate that L1 NBRCS from individual CyGNSS units are well inter-calibrated and remarkably stable over time, a significant improvement over previous versions of the products. However, prominent geographical biases reaching over 3 dB are found in NBRCS, linked to factors including the choice of GPS transmitter and the bistatic geometry. L1 LES shows similar anomalies as well as a secondary geographical pattern of biases. These findings provide a basis for further improvement of CyGNSS Level-2 wind products and have wider applicability to improving the calibration of GNSS-R sensors for the remote sensing of non-ocean Earth surfaces. [ABSTRACT FROM AUTHOR]

Measurements of significant wave height from satellite altimeter missions are finding increasing application in investigations of wave climate, sea state variability and trends, in particular as the means to mitigate the general sparsity of in situ measurements. However, many questions remain over the suitability of altimeter data for the representation of extreme sea states and applications in the coastal zone. In this paper, the limitations of altimeter data to estimate coastal Hs extremes (<10 km from shore) are investigated using the European Space Agency Sea State Climate Change Initiative L2P altimeter data v1.1 product recently released. This Sea State CCI product provides near complete global coverage and a continuous record of 28 years. It is used here together with in situ data from moored wave buoys at six sites around the coast of the United States. The limitations of estimating extreme values based on satellite data are quantified and linked to several factors including the impact of data corruption nearshore, the influence of coastline morphology and local wave climate dynamics, and the spatio-temporal sampling achieved by altimeters. The factors combine to lead to considerable underestimation of estimated Hs 10-yr return levels. Sensitivity to these factors is evaluated at specific sites, leading to recommendations about the use of satellite data to estimate extremes and their temporal evolution in coastal environments. [ABSTRACT FROM AUTHOR]

The UK's TechDemoSat-1 (TDS-1), launched 2014, has demonstrated the use of global positioning system (GPS) signals for monitoring ocean winds and sea ice. Here it is shown, for the first time, that Galileo and BeiDou signals detected by TDS-1 show similar promise. TDS-1 made seven raw data collections, recovering returns from Galileo and BeiDou, between November 2015 and March 2019. The retrieved open ocean delay Doppler maps (DDMs) are similar to those from GPS. Over sea ice, the Galileo DDMs show a distinctive triple peak. Analysis, adapted from that for GPS DDMs, gives Galileo's signal-to-noise ratio (SNR), which is found to be inversely sensitive to wind speed, as for GPS. A Galileo track transiting from open ocean to sea ice shows a strong instantaneous SNR response. These results demonstrate the potential of future spaceborne constellations of GNSS-R (global navigation satellite system–reflectometry) instruments for exploiting signals from multiple systems: GPS, Galileo, and BeiDou. [ABSTRACT FROM AUTHOR]

Abstract: Using wave spectra extracted from image mode ERS-2 SAR scenes, the spatial homogeneity of the wave field in deep water is investigated. From the 100×100km image, several small images of 6.4×6.4km are selected and the wave spectra are computed. The locally disturbed wind velocity pattern, caused by the sheltering effect of large mountains near the coast, translates into the selected SAR image as regions of higher and lower wind speed. Assuming that the swell field is uniform over the whole image, SAR derived swell spectra retrieved from the sheltered and non-sheltered areas are intercompared. Any difference between them could be related to a possible modification associated with the sheltering effect on the wind speed and/or a possible interaction between wind sea and swell, since the wind sea part of the spectrum would be slightly different due to the different wind speeds. The results show that there is no significant modification, and apparently there is no clear difference in the swell spectra despite the different wind sea components and wind speeds. [Copyright &y& Elsevier]