508 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 30, NO. 3, JULY 2005

Measurement of Ocean Surface Winds UsingSynthetic Aperture Radars

Jochen Horstmann and Wolfgang Koch

Abstract—A methodology for retrieving high-resolution oceansurface wind fields from satellite-borne synthetic aperture radar(SAR) data is introduced and validated. The algorithms developedare suited for ocean SAR data, which were acquired at the Cband of either vertical (VV) or horizontal (HH) polarization intransmission and reception. Wind directions are extracted fromwind-induced streaks that are visible in SAR images of the oceanat horizontal scales greater than 200 m. These wind streaks arevery well aligned with the mean surface wind direction. To extractthe orientation of these streaks, two algorithms are introduced,which are applied either in the spatial or spectral domain. Oceansurface wind speeds are derived from the normalized radarcross section (NRCS) and image geometry of the calibrated SARimages, together with the local SAR-retrieved wind direction.Therefore, several C-band models (CMOD_IFR2, CMOD4, andCMOD5) are available, which were developed for VV polarization,and have to be extended for HH polarization. To compare thedifferent algorithms and C-band models as well as demonstratetheir applicability, SAR-retrieved wind fields are compared tonumerical-model results considering advanced SAR (ASAR) datafrom Environmental Satellite (ENVISAT), a European satellite.

Index Terms—Ocean winds, polarization ratio (PR), radarbackscatter, remote sensing, synthetic aperture radar (SAR).

I. INTRODUCTION

TODAY, several satellite-borne scatterometers (SCATs)that can measure ocean surface wind fields with a res-

olution of up to 25 km on a global and operational basisindependent on daylight and cloudiness are available. TheseSCATs cover nearly the entire globe within one day andhave been used on a regular basis for operational wind fore-casting at weather centers such as the European Center forMedium-Range Weather Forecasts (ECMWF). All theseSCATs were originally designed to measure wind speeds on aglobal basis and not to have the resolution necessary to observeshort-scale wind features especially important in coastal areas.However, satellite-borne synthetic aperture radar (SAR) instru-ments offer the unique opportunity to image the ocean surfacewith a very high resolution, typically below 100 m. Since thelaunch of the European remote-sensing (ERS) satellites ERS-l,ERS-2, and Environmental Satellite (ENVISAT), as well as theCanadian Radar Satellite (RADARSAT)-1, SAR images havebeen acquired over the oceans on a continuous basis over thelast 12 years. Their high resolution, together with their largespatial coverage, make them a valuable tool for measuring

Manuscript received December 12, 2004; revised March 29. 2005; acceptedApril 2, 2005. Associate Editor: R. Garello.

The authors are with the GKSS Research Center, Geesthacht 21502, Germany(e-mail: Horstmann@gkss.de).

Digital Object Identifier 10.1109/JOE.2005.857514

geophysical parameters such as ocean surface winds, waves,and sea ice [1]–[3].

All the SAR sensors aboard the satellites mentioned aboveoperate at the C band with either vertical (VV) or horizontal(HH) polarization in transmission and reception. They all op-erate at moderate incidence angles within 15 and 50 . For thiselectromagnetic wavelength ( 5 cm) and range of incidence an-gles, the backscatter of the ocean surface is primarily caused bythe small-scale ocean surface roughness on horizontal scales of5–10 cm. This dominant scattering mechanism is called reso-nant Bragg scattering ([4]) and is defined by:

2 0 (1)

where denotes the resonant Bragg wavenumber, is theelectromagnetic wavenumber, and is the local incidence angleof the electromagnetic radiation from the sensor (for details onocean surface scattering, refer to [5]). The small-scale surfaceroughness is strongly influenced by the local wind field, andtherefore allows the radar backscatter to be empirically relatedto the wind.

In this paper, algorithms for wind-field retrieval from satel-lite-borne SARs operating at the C band with either VV or HHpolarization in transmit and receive are introduced. The algo-rithms are applied to retrieve wind fields from the advancedSAR (ASAR) system aboard the European satellite ENVISAT.To compare the different algorithms as well as demonstrate theirapplicability, ASAR-retrieved wind fields are compared to re-sults of the operational numerical atmospheric model of theGerman Weather Service (DWD).

The paper is organized as follows: In Section II, the utilizedENVISAT ASAR data are described. Section III describes thedifferent SAR wind-retrieval algorithms, which, in Section IV,are applied to ENVISAT ASAR images and compared to theDWD-model results. In Section V, the results are summarizedand an outlook is given.

II. UTILIZED DATA

For the following investigations, ASAR data acquired by theEuropean remote-sensing satellite ENVISAT were used. TheENVISAT satellite operates in a sun-synchronous polar orbitat a height of 800 km. It has an orbital period of 101 minand is operating in a 35-day repeat cycle. The ASAR systemis a right-looking system, which means that the imagery areacquired on the right-hand side with respect to the satelliteflight direction (azimuth) perpendicular to the flight direction.ENVISAT ASAR acquires images at the C band (5.34 GHz)

0364-9059/$20.00 © 2005 IEEE

HORSTMANN AND KOCH: MEASUREMENT OF OCEAN SURFACE WINDS USING SYNTHETIC APERTURE RADARS 509

Fig. 1. High-resolution RAR image of the ocean surface, acquired at grazingincidence with the X-band at horizontal (HH) polarization in transmission andreception. The image is the result of a 60-s time average of 32 RAR images.Wind-induced streaks are clearly visible at scales below 500 m.

and can be operated at different polarization combinations.For this study, 61 ASAR images were acquired at either HHor VV polarization in transmission and reception. All EN-VISAT ASAR imagery utilized were acquired in the ScanSARwide-swath mode, which offers the largest coverage in theacross-track direction (range). ScanSAR images are generatedby scanning the incidence angle and sequentially synthesizingimages for different subswaths at incidence angles between 15and 45 . In ScanSAR wide-swath mode, ENVISAT ASAR canimage a swath width of up to 450 km with a spatial resolutionof 100 m.

III. SAR WIND-FIELD RETRIEVAL

Ocean surface wind retrieval from SAR is a two-step process;in the first step, wind directions are retrieved, which are a nec-essary input in the second step. Wind directions are extractedfrom wind-induced streaks visible in the SAR image at dif-ferent scales, typically 200 m. Wind speeds are retrieved fromthe backscattered normalized radar cross section (NRCS) of theocean surface utilizing a geophysical model function, which de-scribes the dependence of the NRCS on the wind and radarimaging geometry. In the following, these algorithms will bedescribed in detail.

A. Wind-Direction Retrieval

The most popular methods for SAR wind-direction retrievalare based on the imaging of linear features at scales above 400m. Most of these features are associated to wind streaks [6]and marine atmospheric boundary layer (MABL) rolls [7], [8],which are visible in SAR images. In Fig. 1, a real aperture

radar (RAR) image with a resolution of 15 m shows the dif-ferent scales of wind-induced streaks. This RAR image of theocean surface results from the integration of a radar image se-quence consisting of 32 images acquired every 2 s. The timeaveraging was performed to remove the effect of ocean surfacegravity waves. Wind-induced streaks can be seen in the entireimage covering different scales. Studies of Dankert et al. [9] uti-lizing high-resolution RAR imagery have shown that wind-in-duced streaks at scales above 100 m are aligned within 14of the mean surface wind (see also Dankert et al. [10], thisissue). Therefore, in the following, we assume the linear featuresvisible in the SAR images are aligned with the mean surfacewind direction. This conclusion is supported by the results ofDrobinski and Foster [6]. Furthermore, the results of Dankert etal. [9] encourage us to focus on the smallest possible scales thatcan be utilized from space-borne SARs, which are 200 m andare limited by the spatial resolution of the SAR system. Resultsof SAR wind-direction retrieval based on larger scale features

3 km often depict MABL rolls, which are more likely to sig-nificantly differ from the mean surface wind direction [8], [11].

To retrieve the orientation of the linear features visible in SARimages, two methods have been developed, the local-gradientmethod (LG method) [1], [12], which is applied in the spa-tial domain, and the fast-Fourier-transformation method (FFTmethod) [13], [14], which is applied in the spectral domain.

1) LG Method: In a first step, the SAR image is smoothedand reduced to resolutions of 100, 200, and 400 m. This resultsin three SAR images representing spatial scales above 200, 400,and 800 m. From each of these images, local directions, de-fined by the normal to the LG, are computed leaving a 180ambiguity. In the next step, all pixels that are effected by non-wind-induced features, e.g., land, surface slicks, and sea ice, aremasked and excluded from further analysis. Therefore, high-res-olution land masks and SAR image filters, which are describedby Koch [12], are considered. The image filters are extractedfrom the SAR image itself considering locally retrieved parame-ters, e.g., the mean and standard deviation of the image intensityas well as the retrieved LGs. Finally, from all of the resulting di-rections, only the most frequent directions in a predefined gridcell are selected. These resulting wind directions from the 100-,200-, and 400-m SAR images vary typically only by a few de-grees, except for cases where additional features are present inthe SAR image, e.g., ocean surface waves, internal waves, andartifacts of image processing, like scalloping. The 180 direc-tional ambiguity can be removed if wind shadowing is present,which is often visible in the lee of coastlines. If such featuresare not present in the image, other sources, e.g., weather charts,atmospheric models or in situ measurements, have to be takeninto account.

Fig. 2 shows an SAR image collected by the European satel-lite ERS-1 in the marginal ice zone off the coast of Spitzbergen,acquired at the C band with VV polarization. In this case, allthe dark patches visible in the SAR image are due to the sea ice.In Fig. 2(a), the mask resulting from the applied filters is super-imposed to the SAR image. It is clearly visible that most pixelseffected by sea ice are included in the masked area. Fig. 2(b)shows the effect the filters have on the wind directions retrievedvia the LG method. Without consideration of the filters, the wind

510 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 30, NO. 3, JULY 2005

Fig. 2. SAR image acquired by the European satellite ERS-1 in the marginal ice zone off the coast of Spitzbergen. (a) The mask that results from filtering. (b)Wind directions resulting from the LG method with (blue arrows) and without (red arrows) consideration of the filter. (c) Wind directions retrieved from the FFTmethod with (blue bars) and without (red bars) utilizing the filter.

vectors can be significantly off of the mean wind direction (redarrows), while considering the filter (blue arrows) gives, in mostcases, a good estimate of the mean air flow. In areas where themask leaves only a few pixels for the wind-direction retrieval,the methodology fails [upper right of Fig. 2(b)]. However, inmost cases, the application of this mask results in a significantimprovement of the wind direction retrieved.

2) FFT Method: The most popular method for extraction ofthe wind directions from SAR imagery searches for the domi-nant wind-streak direction in the spectral domain and is referredto as the FFT method. This method was first introduced by Ger-ling [15] and later extended and modified by several others (e.g.,in [11], [13], [14], [16], and [17]).

In the first step, all pixels in the SAR image that are affectedby non-wind-induced features, e.g., land, surface slicks, and seaice, are masked using the land mask and filters mentioned above.In the next step, the SAR image is split up into subimages, whichrepresent the wind-direction resolution that is typically set to10 km 10 km. Then, all masked pixels in each subimage arereplaced by the mean intensity value of the selected maskedsubimage. This approach enables the use of the FFT methodclose to the shore and permits us to distinguish between wind-and non-wind-induced pixels such as in the marginal ice zone[Fig. 2(c)]. Finally, a regression is estimated, which is weightedwith the energy densities for wavelengths between 500 and 1800m, representing the scales considered. The threshold of 500 m isset to exclude ocean surface waves, while the threshold of 1800m is set to exclude larger scale features, e.g., MABL rolls, inflec-tion point instabilities, and lee waves, that can be significantlyoff of the mean surface wind directions. The main spectral en-ergy is located perpendicular to the orientation of the streaks,giving the surface wind direction with a 180 directional ambi-guity. Again, the 180 ambiguity can be removed as mentionedabove. In Fig. 2(c), wind directions are plotted as retrieved viathe FFT method with (blue bars) and without (red bars) con-sideration of the filters. It can be seen that, similar to the LGmethod, the wind directions improve significantly when consid-ering the filters.

B. SAR Wind-Speed Retrieval

For wind-speed retrieval, an empirical model functionrelating the NRCS of the ocean surface to the local near-sur-face wind speed , wind direction versus antenna look direction

, and incidence angle is used. The general form of thefunction is

1 2 (2)

where , , , and are coefficients that, in general, dependon radar frequency and polarization. In the case of the modelsavailable for the C band, these coefficients were determined em-pirically by evaluation of ERS-1 SCAT data, which operatesat the C band with VV polarization, and wind fields from theECMWF. The resulting empirical C-band model functions arethe CMOD4 [18] and CMOD_IFR2 [19], which are the mostcommonly used, and the CMOD5 [20], which has recently beendeveloped. These functions are applicable for wind-speed re-trieval from VV-polarized SAR images (see, e.g., [13], [16],and [21]). For wind-speed retrieval from C-band SAR imagesacquired at HH polarization, no similar well-developed modelexists, so that a hybrid model function that consists of one ofthe prior-mentioned empirical models and a C-band polariza-tion ratio (PR) is applied [22]–[24]. The PR is defined as

PR (3)

where and are the HH- and VV-polarized NRCS, re-spectively. So far, the PR is not well known and several differentPRs have been suggested in literature [25]–[27]. The PR pro-posed by Thompson et al. [26] neglects wind-speed and wind-direction dependence and is given by

PR1

1 2(4)

where is a constant and set to 0.6, fitting the measurementsof Unal et al. [28]. This form is closely related to theoreticalforms of the PR, where 0 gives the theoretical PR for

HORSTMANN AND KOCH: MEASUREMENT OF OCEAN SURFACE WINDS USING SYNTHETIC APERTURE RADARS 511

Fig. 3. ENVISAT ASAR images of the southern North Sea acquired on 23 June, 2003 (left-hand side) and 18 July, 2003 (right-hand side) with vertical VVpolarization in transmit and receive. Superimposed to the images are the wind vectors resulting from the ASAR data using the LG method and CMOD4 (bluearrows) as well as the results from the model of the German weather service DWD (red arrows).

Bragg scattering and 2 results in Kirchhoff scattering. Sev-eral different values for have been suggested in the past, con-sidering RADARSAT-1 SAR data; they vary between 0.4 and1.2 [22], [24], [29]. Comparisons of RADARSAT-1 SAR dataof different SAR processing facilities showed that the differentfindings of are most likely due to the different calibrations ofRADARSAT-1 SAR data at different facilities [30], [31]. Re-cently, Mouche et al. [27] suggested a PR that is additionallydependent on the wind direction. Their model was evaluated byconsidering airborne RAR data acquired at the C band with bothVV and HH polarizations covering moderate incidence anglesand a wide range of wind speeds and wind directions (for detailson the model, refer to [27]).

IV. COMPARISON OF ENVISAT-ASAR-RETRIEVED

WIND FIELDS

In the following, wind fields are retrieved from ENVISATASAR images using all of the methods introduced in Section III.The ASAR-retrieved wind fields are compared to the results ofthe DWD model. This permits us to draw conclusions on what isthe most suitable approach for retrieving wind fields from EN-VISAT ASAR data. The resulting methodology is also transfer-able to other well-calibrated C-band SAR systems such as thoseof the satellites ERS and RADARSAT.

A. Examples of ENVISAT-ASAR-Retrieved Wind Fields

In Fig. 3, two example wind fields are shown, which were re-trieved from ENVISAT ASAR data acquired over the southernNorth Sea at VV polarization. The image on the left-hand sidewas acquired on 23 June, 2003, at 21:14 coordinated universaltime (UTC), and shows the North Sea coasts of Germany (in thesouth) and Denmark (in the east). The image on the right-handside was acquired on 18 July, 2003, at 10:03 UTC, covering

a similar area. The ASAR-retrieved wind vectors are superim-posed to the ASAR image (blue arrows) as well as the vectorsresulting from the DWD model (red arrows). The ASAR windfields were retrieved from the area corresponding to the gridcell in the DWD-model output, which corresponds to an av-erage grid-cell size of approximately 45 km 75 km. Winddirections were retrieved using the LG method and the windspeeds using the CMOD4 model. The directional ambiguitiesof the ASAR-retrieved wind directions were removed by con-sidering wind shadowing at the coast in the case of the 23rd ofJune, and the numerical-model results in the case of the 18th ofJuly. The model results are only available on a 6-h basis so thatthe model wind fields were interpolated to the exact ASAR ac-quisition times. In both cases, the ASAR-retrieved winds agreevery well with the wind fields resulting from the DWD model.

To demonstrate the high-resolution capability of ASAR wind-field retrieval, a subimage from the 23rd of June (dashed box inFig. 3 left-hand side) was used to retrieve wind fields with a reso-lution of 5 and 10 km, respectively (Fig. 4). The wind directionsare very similar to the directions retrieved on the coarse grid ofthe DWD model; however, towards the coast, there is a slightchange of wind directions towards the South and the overallvariability is significantly larger. The higher variability in winddirection at the 5-km resolution is also due to the wind-direc-tion algorithm, which needs a certain area for a good estimateof the orientation of the streaks. Wind speeds are also very sim-ilar to the results in Fig. 3, and show a larger variability, which,in the case of the 5-km resolution, is also due to the error in winddirections.

B. Comparison of the SAR Wind-Retrieval Methods

To compare the two wind-direction algorithms as well as thedifferent wind-speed models and PRs, a set of 61 ENVISAT

512 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 30, NO. 3, JULY 2005

Fig. 4. High-resolution wind fields as retrieved from a cutout of the ENVISATASAR image depicted in Fig. 3 (left-hand side). Wind-field resolutions of 10 km(upper plot) and 5 km (lower plot) were considered.

ASAR ScanSAR images was utilized in comparison to themodel results of the DWD. Only nine ASAR images wereacquired at HH polarization, which enabled only a limitedcomparison of the different PRs. Again, the ASAR wind fieldswere retrieved from the area corresponding to the grid cell inthe DWD-model output, resulting in an average grid-cell sizeof approximately 45 km 75 km. The wind fields from theDWD represent 6-h analyzed wind fields that were interpolated

Fig. 5. Scatter plot of wind directions resulting from the DWD model and theASAR images. Wind directions were retrieved using (a) the LG method and(b) the FFT method, respectively.

TABLE IMAIN STATISTICAL PARAMETERS OF THE COMPARISON OF WIND-DIRECTION

RETRIEVAL USING THE LG AND FFT METHODS

to the ASAR acquisition times. Wind directions were retrievedusing both the LG and FFT methods. To remove the directionalambiguities of the ASAR-retrieved wind directions, the resultsof the DWD model were taken into account. Wind speeds wereretrieved by taking the ASAR-retrieved wind direction and themean NRCS and incidence angle of each grid cell as inputto the C-band model. In the case of images acquired at HHpolarization, the PR was considered.

For comparison of the wind-direction algorithms, only 12ASAR images were considered, which showed that the FFTmethod is unsuited to retrieve wind directions from ENVISATASAR ScanSAR images. In Fig. 5(b), the scatter plot of winddirections resulting from the DWD model and the FFT methodis shown. The FFT method is strongly affected by scalloping,which is visible in most of the utilized ENVISAT ASAR im-ages. Scalloping is an SAR processing artifact, which causes the

HORSTMANN AND KOCH: MEASUREMENT OF OCEAN SURFACE WINDS USING SYNTHETIC APERTURE RADARS 513

Fig. 6. Scatter plots of wind speeds resulting from the DWD model versus wind speeds from ENVISAT ASAR. The ASAR wind speeds were retrieved using the(a) CMOD_IFR2, (b) CMOD4, and (c) CMOD5 models. The PRs according to Thompson et al. [26], with � = 0.6, were taken for the ASAR images acquired atHH polarization. The dotted and dashed lines give the linear regressions considering all and only HH-polarized data.

appearance of linear features aligned in the range direction, witha spacing within the scales evaluated by the FFT method. Thisoften leads to a misinterpretation of FFT-derived wind direc-tions. The effect of scalloping can be detected in Fig. 5(b), whereSAR wind directions of approximately 90 and 270 occur quiteoften, and which are approximately in the range direction of theacquired ASAR scenes. In the case of the LG method, scallopingonly affects subimages with resolutions below 400 m, which en-ables the algorithm to overcome this handicap. The main statis-tical parameters resulting from the comparisons of the 12 EN-VISAT ASAR images with respect to the wind-direction re-trieval from the LG and FFT methods are given in Table I. In thecase of the LG method, all ASAR images were considered andcompared to the DWD-model results. If all grid cells are takeninto account (including grid cells partially covered by land), thestatistics do not look as good as when grid cells covered by morethan 50% land are excluded from the comparison [Table I andFig. 5(a)]. This result is not unexpected because the coastal areasare affected by a higher variability, which cannot be resolved bythe numerical model due to its too-coarse resolution. Overall,the comparison of wind directions from ASAR using the LGmethod to the wind directions from the DWD model are verypromising, having a correlation coefficient of 0.95 with a negli-gible bias and a root-mean-square error of 18.3 .

For the wind-speed comparison, all ASAR images were con-sidered and the wind speeds were retrieved using the wind direc-tions from the LG method as input to the different model func-tions. In the first step, the CMOD_IFR2, CMOD4, and CMOD5models are compared to each other. In the case of HH-polar-ized ASAR images, the PR according to (4), with 0.6, wasused. The resulting scatter plots are depicted in Fig. 6, wherethe models from left to right are CMOD_IFR2, CMOD4, andCMOD5. The ASAR VV- and HH-polarized images have beendifferentiated to point out the different results concerning polar-ization. The green line gives the linear regression consideringall data and the red line, only considering HH-polarized data.The main statistical parameters of the comparisons are listed inTable II, again distinguishing between the different models aswell as between VV and HH polarizations.

The largest differences between the different C-band modelsoccur at wind speeds over 10 m s . At moderate wind

TABLE IIMAIN STATISTICAL PARAMETERS OF THE WIND-SPEED COMPARISON

CONSIDERING THE C-BAND MODELS

speeds, they agree fairly well. The CMOD_IFR2 and CMOD5models are very similar to each other. The main differenceoccurs at very high wind speeds 20 m s , where CMOD5estimates higher winds. Comparison of CMOD4-retrievedwind speeds to CMOD_IFR2-retrieved wind speeds shows thatCMOD_IFR2 predicts, on average, slightly higher wind speedsbias 0.4 m s . Only for wind speeds below 4 m s

does CMOD4 give higher wind speeds. Comparison of theCMOD_IFR2- and CMOD5-retrieved wind speeds shows thatCMOD5 gives higher wind speeds at low 4 m s andhigh 17 m s wind speeds. Concerning the statisticsresulting from the comparison of DWD wind speeds to ASARwind speeds retrieved by each of the C-band models (Table II),CMOD4 gives the best results. However, especially at highwind speeds, CMOD4 underestimates the wind speeds signif-icantly. CMOD_IFR2 and CMOD5 give better results at highwind speeds but still underestimate the wind speed.

The comparison with respect to the different PR models wasperformed using the wind directions resulting from the LGmethod and the CMOD4 model. Again, all ASAR images wereconsidered, and the resulting wind speeds were compared tothe DWD-model results. In Fig. 7, the scatter plots are givenwhere the polarization according to Mouche et al. [27] (A)and Thompson et al. [26] using 0.6 (B) and 1.0 (C)were used. The corresponding main statistical parameters withrespect to all (VV- and HH-polarized data) and HH-polarizeddata are listed in Table III. In this case, only grid cells coveredat least by 50% water were considered. The main differencesbetween the PRs are seen in the biases, while the standarddeviation of the PRs are very similar. It is significant that all

514 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 30, NO. 3, JULY 2005

Fig. 7. Scatter plots of wind speeds resulting from the DWD model versus wind speeds from ENVISAT ASAR. The ASAR wind speeds were retrieved using theCMOD4 model. The polarization ratios according to (a) Mouche et al. [27] and Thompson et al. [26] using (b) � = 0.6 and (c) � = 1.0 were considered for theASAR images acquired at HH polarization. The dotted and dashed lines give the linear regressions considering all and only HH-polarized data.

TABLE IIIMAIN STATISTICAL PARAMETERS OF THE WIND-SPEED COMPARISON

CONSIDERING THE PR MODELS

PRs lead to an underestimation of the wind speeds. Taking theroot-mean-square error as reference, the most suitable PR isaccording to Thompson et al. [26] using 0.6. However,due to the limited ASAR data available, these results have to bevalidated considering a larger set of ASAR data.

V. CONCLUSION

Two methods for retrieving wind directions from wind-in-duced streaks visible in SAR data have been introduced. The LGmethod retrieves the orientation of the wind streaks in the spatialdomain, considering the LGs at a grid-cell size of 100, 200, and400 m, corresponding to scales 200 m. The FFT method ex-tracts the wind direction in the spectral domain, searching for thedominant spectral peak at wavelengths between 500 and 1800m. Both methods work significantly better if the filters proposedby Koch [12] are considered. Filtering is especially importantwhen retrieving wind fields in the marginal ice zone as well asat low wind speeds, when non-wind-induced features are morelikely to occur [32]. Comparison of SAR-retrieved wind direc-tions to the DWD-model analysis resulted in a root-mean-squareerror of 18.3 with a bias of 6.1 for the LG method. Com-parison of the FFT method showed a root-mean-square error of39.1 when comparing a subset of the ASAR data. An in-depthanalysis of the imagery showed that the FFT method is stronglyaffected by scalloping, which was present in most of the ac-quired ASAR ScanSAR imagery. Therefore, the FFT methodis not suited for most ENVISAT ASAR ScanSAR data.

For wind-speed retrieval, the C-band models CMOD_IFR2,CMOD4, and CMOD5, which were developed for the VV-po-

larized C-band SCATs aboard ERS, were utilized. As input tothese models, the NRCS, incidence angle, and wind directionis needed, which can be extracted from the ASAR data. Com-parison of ASAR-retrieved wind speeds to DWD-model anal-ysis resulted in a root-mean-square error of 2.11 m s witha negligible bias using the CMOD4 model. Both CMOD_IFR2and CMOD5 show slightly larger errors. It is very likely thatthe scarce resolution of the DWD model is too coarse to resolvesmall-scale features, e.g., wind shadowing, which occurs espe-cially near the coasts.

Comparison of the PR showed that all PRs lead to an under-estimation of wind speeds retrieved from HH-polarized images.Due to the limited number of available HH-polarized ASARdata, a final conclusion on the best suited PR cannot be drawn.However, all PRs enable the retrieval of the estimate of the sur-face wind speed with a similar accuracy.

The good agreement of ASAR-retrieved wind directions andwind speeds to the DWD-model results shows the applicabilityof the LG method together with the CMOD4 model. The ob-tained ASAR wind-retrieval errors are in the same magnitudeas the results achieved by satellite-borne SCATs.

Future investigations will have to concentrate not only onthe validity of the C-band models, especially concerning highwind speeds and polarization, but also effects due to fetch lim-itations. Concerning the wind-direction retrieval from wind-in-duced streaks, an investigation as to which scales are the mostappropriate to infer the near-surface wind direction has to becarried out. Furthermore, the accuracy of the wind fields at dif-ferent spatial resolutions has to be investigated.

ACKNOWLEDGMENT

The ENVISAT ASAR data were provided by the EuropeanSpace Agency within the project BIGPASO. All numericalatmospheric-model results were kindly made available by theGerman Weather Service.

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Jochen Horstmann received the Diploma degree inphysical oceanography and the Ph.D. degree in earthsciences, both from the University of Hamburg, Ham-burg, Germany, in 1997 and 2002, respectively.

In 1995, he joined the GKSS Research Center,Geesthacht, Germany, where he has been a ResearchScientist in the Institute for Coastal Research since2000. In 2002, he was a Visiting Scientist at theApplied Physics Lab, John Hopkins University,Baltimore, MD, and from 2004 to 2005, at theRosenstiel School of Marine and Atmospheric

Sciences, University of Miami, Miami, FL. His main research interests are inextraction of geophysical parameters from radar sensors.

Wolfgang Koch received the State Examinationin mathematics, physics, and educational sciencedegree from the University of Hamburg, Hamburg,Germany, in 1988.

In 1987, he joined the boundary layer group atthe GKSS Research Center, Geesthacht, Germany.He has participated in numerous wave-modelingprojects, worked in verification of wind and wavemeasurements with altimeters, and is currently in theInstitute for Coastal Research, working on estimationof geophysical parameters from SAR images.