A Wavelet-Based Technique for Sea Wind Extraction from SAR Images

Abstract

We present the follow-up of our previously published work, where we described a wavelet-based method to characterize the sea surface backscatter structures in Synthetic Aperture Radar (SAR) images. The method relies on the ability of the 2-D continuous wavelet technique to detect the spatial structure of the Marine Atmospheric Boundary Layer (MABL) and to isolate wind-related cells and features. The analysis of the cells' geometry, molded by the radiometric characteristics of the sea surface, permits the identification of the wind direction inside the cells, due to the along-wind asymmetry of backscatter structures, and thus the computation of the wind speed through standard algorithms. Twenty-one SAR images (ERS-2 and Envisat ASAR Wide Swath) over the Mediterranean Sea have been analyzed, and the results are compared with satellite wind fields. The images cover a range of meteorological conditions from low to moderate winds. Comparison of the SAR-derived wind fields with those provided by satellite scatterometers indicates a good score of success (roughly 70%-80%). The developed methodology, once tested over an adequate number of images to derive statistically reliable results, could be routinely used to enrich SAR images with the wind field as well as to characterize the MABL in terms of size, distribution, and shape of the backscatter cells.

Numerical models sea surface wind compared to scatterometer observations for a single Bora event in the Adriatic Sea

“…On the other hand, satellite-borne Synthetic Aperture Radars (SAR), have a much finer spatial resolution (from metres to hundreds of metres, depending on the sensor); however, their images are not frequent; their acquisition has often to be agreed in advance with the space agencies; the use of their full spatial resolution would be unpractical for operational use; finally, there is no standard SAR wind product operationally provided by the agencies: the algorithms to extract the wind direction from them are still in a development phase and depend on external sources of information (Zecchetto and De Biasio, 2008).…”

Wind Direction Extraction from SAR in Coastal Areas

“…The methods used in this work to estimate the wind directions over the sea area by SAR refines the technique described in [13,14] by using a full 2D-CWT. This method is applied to a SAR image to obtain the wavelet spectrum, which is the map of energy as a function of angle (from -90 to 90 degrees, which is the fourth and the first quadrant of the Cartesian coordinate system) and scales chosen in the range between 200 m to 2500 m. The choice of scales length roughly corresponds to the wind frequency range between 10 −2 and 10 −3 Hz, where the small scale maximum of the wind spectra lies (see [10], p. 85).…”

“…This tool is the 2D Continuous Wavelet Transform (hereafter 2D-CWT), a powerful technique developed in the late 1980s [11,12], able to provide the energy-wavelength information as a function of the location. Applied to SAR images, this method has been used in a quasi 2D version [13,14], providing interesting results with, however, important shortcomings. With respect to these works, this paper uses the full 2D-CWT, at that time not available, which makes the method more robust, solving the shortcomings made evident by using the quasi 2D version of CWT; moreover, it is focused on coastal areas and deals with much more images (61 vs. 21) from different satellites than the previous papers.…”

“…The issue of wind direction estimation from SAR images is not new, but the techniques to estimate the wind directions from SAR [13][14][15][16][17][18][19][20] are not exhaustive yet.…”

Ocean Wind Fields from Satellite Active Microwave Sensors

“…Scientific literature about SAR images over the ocean has shown a variety of geophysical phenomena detectable by SAR (Alpers & Brümmer, 1994;Kravtsov et al, 1999;Mitnik et al, 1996;Mityagina et al, 1998;Mourad, 1996;Sikora et al, 1997;Zecchetto et al, 1998), including the multiscale structure in the atmospheric turbulence under high winds and the structure of the convective turbulence under low wind. More recently, some effort has been devoted to evaluate the wind direction, using the backscatter signatures produced by the atmospheric wind rolls or those occurring at the lee side of islands (Vachon & Dobson, 2000) as effect of wind shielding, by computing the local gradient of the image backscatter (Horstmann et al, 2002;Koch, 2004) or by using the two dimensional Continuous Wavelet Transform (CWT2) (Zecchetto & De Biasio, 2002;Zecchetto & De Biasio, 2008). This section illustrates the ability of the CWT2 in detecting and quantifying the backscatter pattern linked to the spatial structure of the MABL.…”