Wind Direction Analysis over the Ocean using SAR Imagery

Transcription

1 Journal of Information & Computational Science 5: 1 (2008) Available at http: Wind Direction Analysis over the Ocean using SAR Imagery Kaiguo Fan a,b,, Weigen Huang a, Mingxia He b, Bin Fu a a State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou , China b Key Laboratory of Ocean Remote Sensing, Ministry of Education, Ocean University of China, Qingdao , China Abstract Currently, the retrieval of the ocean surface wind fields form Synthetic Aperture Radar (SAR) imagery suffers from inadequate knowledge of the wind direction. Usually, wind directions could be taken from point measurements, meteorological numerical models and scatterometer wind data, but generally they would be at a different time, have much coarser resolution or the values may be suspect. Fortunately there are a couple of processes that cause a signature aligned with the wind direction. In most SAR images of the seas, these wind induced phenomena are visible and could be used to estimate the wind direction, which allows the retrieval of a high resolution wind field, especially for coastal regions. This paper describes both a spectral method by Fast Fourier Transform (FFT) within the spectral domain and a spatial method based on the local gradient working in the spatial domain, these lead to wind directions estimate from SAR imagery, which establish a more extensive foundation for retrieving high-resolution wind fields. Keywords: SAR; Wind Direction; Geophysical Model Function; Local Gradient; FFT 1 Introduction Ocean surface winds are an important parameter in studies of ocean surface variables such as waves, ocean circulation, and marine meteorology and atmosphere-ocean interactions. Ocean winds are measured from spaceborne active and passive microwave instruments; scatterometer, altimeter, synthetic aperture radar, and radiometers [1-3]. Altimeter winds can have nearly 7-km Corresponding author. addresses: van.fkg@tom.com (Kaiguo Fan) / Copyright 2008 Binary Information Press January, 2008

2 224 K. Fan et al. / Journal of Information & Computational Science 5:1 (2008) along-track resolution, but lack directional information and have virtually no swath extent. Microwave radiometers, for example, the Windsat radiometer, can measure ocean wind fields at the same spatial resolution as the scatterometer, about 25 km. The scatterometer can determine marine surface wind fields and is the best tool to retrieve global winds for assimilation into forecast models for a variety of weather and climate studies [1]. However, the spatial resolution is low, and wind estimates close to the coastline are not possible. In recent years, SAR image has been used to estimate ocean wind fields, and has proved the potential for producing wind fields that are unique with respect to spatial resolution and coverage [4]. Much effort has been invested in the derivation of wind speed from SAR images. One approach for extracting winds from SAR images is based on an empirical relationship, also denoted the Geophysical Model Function (GMF), between wind vectors and the Normalized Radar Cross Section (NRCS) of SAR image, for example CMOD-INFR2 [5]. Because this kind of GMF model depends on the incidence angle, radar wavelength, and polarization as well as both wind speed and direction, it is impossible to uniquely determine the wind from a single SAR image. An obstacle to the retrieval of complete wind fields using SAR images is the lack of knowledge of the wind direction. Methods in use are, for example, assuming a fixed direction from a measurement for the whole SAR image, using scatterometer wind direction data or interpolation of the direction of the wind fields from a numerical weather model [6]. None of these methods is really satisfactory, as the information is too sparse, is not available close to the coastlines, and generally is not at the right time so that one cannot resolve small scale variations such as those due to convection cells, wind fronts, and topographic effects in coastal regions. Estimating the wind direction directly from the SAR image being studied is a much more attractive approach. This opportunity arises because the short gravity waves responsible for the backscatter of the radar signal are created instantly by the wind stress and are therefore good indicators of the sea surface wind directions [7]. Previous works concerned with estimating wind directions from SAR images have used FFTs [8] or the local gradient method [6]. However, space-borne SAR systems typically operate at a single look direction, so the estimated wind directions have a 180 ambiguity [8]. Usually, the ambiguity of wind direction is eliminated by using some other wind data, such as National Center for Environmental Prediction (NECP) and European Centre for Medium-Range Weather Forecasts (ECMWF) wind direction data. Ancillary information from buoys, scatterometers or analysis of shadowing effects at the coast are also used to remove the direction ambiguity. In this paper, the retrieval of ocean surface wind direction from SAR images based on both the spectral and spatial domain has been analyzed, which demonstrates the potential to estimate the wind direction directly from the SAR images and establish a more extensive foundation for retrieving high-resolution wind fields using SAR imagery. 2 GMF Model Analysis In the incidence angle range of the SAR, the reflected amount of energy from the sea surface is

3 K. Fan et al. / Journal of Information & Computational Science 5:1 (2008) proportional to the surface wind speed. From a large number of collocations between scatterometer wind data and buoy/model wind data, an empirical GMF model between the wind speed and the NRCS has been established. The result of these efforts was the development of the C-band VV-polarization COMD function [5,9]. However, for HH polarization the GMF model of CMOD is modified by a polarization ratio conversion factor [10]. σ 1+ a tan θ 1+ 2tan θ H = ( ) σ 2 V Where θ is the incidence angle and parameter α is zero for Bragg scattering theory, whereas α is 2, assuming Kirchhoff scattering theory [10,11]. With this modification, wind filed are extracted by the same method as used for VV polarization [11,12]. Originally, the COMD-INFR2 GMF model was developed to retrieve wind vectors from the C-band VV polarization wind scatterometer aboard the ERS-1 satellite [5], and may be expressed as 0 A( θ, u) σ θφ θ φ θ φ V (,, u) = 10 [1 + B(, U)cos( ) + C(, U)cos(2 )] (2) where U is the wind speed at 10 m reference height, A(θ,U),B(θ,U),C(θ,U) are coefficients that depend on U, radar frequency, polarization and incidence angle θ, Φ is the relative angle between the wind direction and the radar look direction. We can see that under a given NRCS, wind direction and radar geometry, the wind speed can be predicted empirically. Experimental results also demonstrate that the wind speed is in coordinately dependent on wind direction, Fig. 1 depicts the variation distribution of wind speed under different wind directions. (1) Fig. 1 Wind speed changes with directions for a given radar signal strength It clearly shows that, under a given radar signal strength (NRCS), the minimum wind speed field is generated by setting the wind direction field to be directly opposite to the radar look direction. The maximum wind speed field is generated by setting the wind direction field to be perpendicular to the radar look direction. And the difference between the maximum and minimum wind speed is about 5 m/s, which shows that the wind direction is very important for the wind speed retrieval from SAR images. As for wind inversing from SAR imagery using the GMF model, such as COMD-INFR2, the basic approach for this inversing problem is to vary the input parameters in the forward GMF until it agrees, as nearly as possible, with observations (i.e. SAR images). Fig. 2 gives the flowchart of SAR wind retrieval.

4 226 K. Fan et al. / Journal of Information & Computational Science 5:1 (2008) Fig. 2 The flowchart of SAR wind speed retrieval The flowchart contains three procedures, (1) wind direction determination and antenna incidence angle calculation, (2) radiometric calibration of SAR image and (3) wind speed output. European Space Agency (ESA) has made a package of routines available for their users for the radiometric calibration and incidence angel [13]. It is clear that estimating the wind speed by inversion of the GMF model still requires a prior knowledge of the wind direction; however, the lack of wind direction information hindered the application of NRCS measurements for the quantitative retrieval of wind speed. 3 Wind Direction Estimation In recent years, several algorithms have been developed and applied for SAR wind direction retrievals; mainly they make use of wind-induced streaks that are visible in SAR images of the ocean at horizontal scales greater than 200 m. These linear features in SAR imagery are very well aligned with the mean surface wind direction [7]. To extract the orientation of these streaks, two methods are introduced; one is applied in the spatial domain and the other in the spectral domain. We shall consider these two methods in term of an example. Also, in order to match the gird cells of high resolution wind fields, the investigated local sub-scene SAR images may be tailored. Together with the local sub-scene SAR retrieved wind direction, high resolution ocean surface wind speeds may be derived from the whole SAR images. Fig. 3 shows one sub-scene SAR image with pixels. And Tab. 1 gives the basic parameters of sub-scene SAR image. Fig. 3 Sub-scene SAR image with pixels

5 K. Fan et al. / Journal of Information & Computational Science 5:1 (2008) Table. 1 Basic parameters of sub-scene SAR image sensor date time UTC orientation heading center long. center lat. ERS-2 SAR descending Spectral method using FFT For the spectral method, the wind direction is estimated from the orientation of low frequency and linear signatures in the whole sub-scene SAR imagery. And orientation of low frequency could be determined by the equation below M N lm, = jk, 2 ( jl km)/ N (3) j= 1 k= 1 Y X e π here, Y and X respectively represents wave number spectral of sub-sar image and the intensity of the sub-scene SAR image. Firstly, the radiometric calibration for the sub-scene SAR image to convert the image data to NRCS was carried out. Since the wind rows are large compared to the pixel resolution, we averaged over eight pixels in the range and in the azimuth directions, then we removed the low frequency variability in the imagery by applying a Wiener filter. Secondly, Fast Fourier Transforms were performed for the local sub-scene SAR image. The objective of this step is to reduce the noise for the high spectral peak. Fig. 4 shows the sub-scene SAR image spectra. The orientation of the spectral peak can be easily seen from Fig. 4, and this provides us with the wind direction, however, the wind direction has a 180 ambiguity. Fig. 4 Sub-scene SAR image spectra and the arrow line indicates the retrieval wind direction Thirdly, the 180 ambiguity in the wind direction is removed by using the NECP wind direction data. The sub-scene SAR image area was within the NECP wind fields of Fig. 5, and the time lag between the SAR imaging and NECP data was about two and half hours.

6 228 K. Fan et al. / Journal of Information & Computational Science 5:1 (2008) Fig. 5 Wind field from NECP data and Sub-scene SAR image within the box 3.2 Spatial method using local gradient For the spatial method, we can think of the ideal image of a streak as being constant along its direction and most strongly varying at right angles to its direction. As the direction of strongest increase is given by the local gradient, the direction of the wind streak is orthogonal to the direction of the most maximum local gradient [14]. The wind direction, assumed to the parallel to the wind streak, is thus also perpendicular to the direction of the local gradient. The spatial method computes the local gradients with standard image algorithms and chooses the orthogonal of the most frequent maximum gradient direction to the possible wind direction [6,15]. Meanwhile, for the local gradients computation, we save any special consideration for 180 ambiguity, so the wind direction estimated from sub-scene SAR image also has the 180 ambiguity, which could also be removed by the NECP wind direction data and some others. For the local gradients computing, the optimized Sober operators and its transpose were used D x = Dy = D T x Using above both equations, the local gradients are computed from the amplitude of SAR image I. and then, four steps were applied (4) (5) G = ( Dx + idy)*( I) 2 G' = S*( G ) 2 G'' = S* ( G ) c = G'/ G'' (6) Where, S is a specific smoother [15] and I represents the amplitude of sub-scene SAR image, c is the systematic parameters. If the local gradients is also a constant, c=1. Having computed all local gradients, the first and last two rows and columns of the image are discarded, for the

7 K. Fan et al. / Journal of Information & Computational Science 5:1 (2008) convolutions applied are exact only at inner image points. Then, the main direction for a sub-image is determined by the position of the maximum in the smoothed histogram of the local gradients weighted systematic parameter c. As a case study, the local gradients method to the same sub-scene SAR images which was used for the spectral method was also applied. Fig. 6 shows the histogram of the local gradients weighted the systematic parameter. As the histogram tends to be somewhat spiky, it is binomially smoothed before the maximum is taken. So the wind direction is defined by the position of the maximum of the smoother histogram. Fig. 6 Histogram of the local wind directions, the position of the maximum of the smoother histogram is the wind direction 4 Discussions and Conclusions Wind directions has been estimated from the same sub-scene SAR image using both the spectral method by FFT and the spatial method by local gradient, and both wind directions were 161 and 159 respectively, which are coincident with each other and also show that both spectral and spatial method are feasible for determining wind directions from SAR imagery. State-of-the-art spectral analysis using FFT works well on regularly shaped regions, such as wind-induced streaks that are visible in SAR images. And this is mainly based on the orientation of low frequency of the whole sub-scene SAR images in spectral domain. Compared with the spectral method, the spatial method of direction estimation is not dependent on the orientation of low frequency, but was determined by the local gradient in the spatial domain. So the spatial method could not only hand wind caused regularly shaped features but also can be applied to wind induced irregularly shaped features close to the coastline especially in shallow water area. Both methods have been at hand for estimating the wind directions directly from SAR images. It gains right time, robustness, flexibility, and spatial resolution by direct evaluation SAR image in the domain of both spectral and spatial, together with external information such as NECP wind data or some other wind direction data removing the direction ambiguity, enables high resolution wind fields directly retrieved from the whole SAR images. Meanwhile, the investigated sub-scene SAR images may be tailored to match the grid cells of numerical meteorological models, thus

8 230 K. Fan et al. / Journal of Information & Computational Science 5:1 (2008) allowing comparison with or assimilation into models without spatial interpolation. Hence, both methods give a promising approach to the techniques of wind direction, which established a more extensive foundation for retrieving high-resolution wind fields using SAR imagery. Acknowledgement The authors would like to thank China Remote Satellite Ground Station, Chinese Academy of Sciences (CAS), for providing the ERS-2 SAR image and thank National Oceanic and Atmospheric Administration (NOAA) for providing the NECP wind data. The authors are also grateful for the anonymous reviewers who made constructive comments. References [1] Bentamy A., Queffeulou P., Quilfen Y., et al, Ocean surface wind fields estimated from satellite active and passive microwave instruments, IEEE Trans. Geosci. Remote Sens., 1999, 37(5): [2] Vachon P. W. and Dobson F. W., Validation of wind vector retrieval from ERS-1 SAR images over the ocean, Global Atmos. Ocean Syst.,1996, 5: [3] Horstmann J., Schiller H., Schulz-Stellenfleth J., et al, Global wind speed retrieval from SAR, IEEE Trans. Geosci. Remote Sens., 2003, 41(10): [4] Donals R.T. and Robert C.B., Mapping high-resolution wind fields using synthetic aperture radar, Johns Hopkins Apl Tech. Dig., 2000, 21: [5] IFREMER-CERSAT, Off-line wind scatterometer ERS products: user manual, Technical Report C2-MUT-W-01-IF, 1999, Version 2.1, IFREMER-CERSAT. [6] Wolfgang K., Directional analysis of SAR images aiming at wind direction, IEEE Trans. Geosci. Remote Sensing, 2004, 42(4): [7] Alpers W. and Brummer B., Atmospheric boundary layer rolls observed by the synthetic aperture radar aboard the ERS-1 SAR satellite, J. Geophys. Res., 1994, 99(C6): [8] Vachon P.W. and Dobson F.W., Validation of wind vector retrieval from ERS-1 SAR images over ocean, The Global Atmosphere and Ocean System, 1996, 5: [9] Stoffelen A. and Anderson D.L.T., Scatterometer data interpretation: estimation and validation of the transfer function COMD-4, J. Geophys. Res., 1997, 102: [10] Unal C.M.H., Snooji P. and Swart P.J.F., The polarization-dependent relation between radar backscatter from the ocean surface and surface wind vectors at frequencies between 1 and 18 Ghz, IEEE Trans. Geosci. Remote Sensing, 1991, 5: [11] Monaldo F. M., Thompson D. R., Beal R. C., et al, Comparison of SAR derived wind speed with model predictions and ocean buoy measurements, IEEE Trans. Geosci. Remote Sens., 2001, 39: [12] Horstmann J., Lehner W. Kock, S., and R. Tonboe, Wind retrieval over the ocean using synthetic aperture radar with C-band HH polarization, IEEE Trans. Geosci. Remote Sens., 2000, 38: [13] Laur H., Bally P., and Meadows P., et al., Derivation of the Backscattering Coefficient σ0 in ESA ERS

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