In this paper mesoscale wind fields are retrieved from SAR data acquired by the satellites RADARSAT, ERS and ENVISAT. Wind directions are extracted from wind induced streaks, e.g. from boundary layer rolls, Langmuir cells, or wind shadowing, using a local gradient method. Wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated ScanSAR images, together with the local wind direction by inversion of semi empirical C-band models, e.g. CMOD4 or CMOD_IFR2, which describe the dependency of the NRCS on wind. These models were developed for the scatterometer aboard the European remote sensing satellites ERS-1 and ERS-2, which operate at C-band with vertical polarization in transmit and receive. To apply these models to ScanSAR data, they have to be modified for horizontal polarization, which was performed considering several C-band polarization ratios including theoretical and empirical forms. To improve and verify the algorithm, wind speeds were computed from several RADARSAT-1 ScanSAR wide swath A images and compared to collocated measurements from the scatterometer aboard ERS-2 and to results obtained with numerical atmospheric models. Using the collocated measurements, the polarization ratio was estimated and applied to improve the wind retrieval algorithm.
The high resolution wind information is used to analyze small scale atmospheric processes like turbulence. The study concentrates on practical applications of these measurements like estimating the fatigue loading of off-shore wind farms, i.e. wind turbines producing renewable energy.
The high coverage of scansar data is used to analyze large scale atmospheric phenomena like hurricanes. The wind field estimation technique is applied to images acquired at subsequent orbits showing the dynamics of the hurricane.