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This paper presents a comparison of existing algorithms to derive surface winds from synthetic aperture radar (SAR) satellites and investigates their use in drift forecasting for search and rescue purposes. Specifically, SAR-derived winds from RADARSAT-1 and ENVISAT advanced synthetic aperture radar (ASAR) data at 1.5 km resolution are compared with scatterometer-derived winds. Three approaches were used to retrieve the wind vector from the SAR data, including an optimal inversion method combining SAR data and background numerical weather prediction, the geophysical model function CMOD-IFR2 with an a priori wind direction, and a technique that uses the backscatter values corresponding to two neighboring subimages with slightly different incidence angles. Our comparisons of SAR wind mapping with scatterometer winds from QuikSCAT and ERS-2 produced a root mean square error (RMSE) of 1.5 m/s. The optimal inversion method seems very promising and appears to be the best choice for assimilation of SAR-derived winds into operational wind products with respect to the datasets presented here. Additionally, the suitability of SAR imagery for search and rescue operations is reviewed. It is recommended that a method should be explored to automatically assimilate such data into operational search and rescue tools. Use of SAR winds in a search and rescue drift model is shown herein to produce improved drift trajectories on a number of search and rescue targets (e.g., life boat, sail boat, person in water). Résumé. Cet article présente une comparaison de trois algorithmes existants pour estimer les vents de surface à l’aide des satellites radar à synthèse d’ouverture (RSO) et leur utilisation pour la prévision de la dérive des objets marins lors des activités de recherche et sauvetage. Spécifiquement, les vents sont extraits de données RSO de RADARSAT-1 et ENVISAT ASAR, à une résolution de 1,5 km, et sont comparés aux vents issus de diffusiomètres. Trois approches ont été employées pour extraire le vecteur de vent des données de RSO, incluant une méthode optimale d’inversion combinant des données d’un modèle de prévision numérique du temps et les données RSO, le modèle géophysique CMOD-IFR2 en utilisant une information a priori sur la direction du vent, et finalement en utilisant les valeurs de rétrodiffusion correspondant à deux sous-images voisines aux angles légèrement différents d’incidence. Nos comparaisons de vent-RSO avec les vents des diffusiomètres QuikSCAT et d’ERS-2 indiquent une erreur moyenne de l’ordre de 1,5 m/s. La méthode optimale d’inversion semble très prometteuse et est probablement le choix le plus approprié à l’assimilation des vents RSO dans les produits opérationnels de vent. L’utilisation du vent-RSO montre une légère amélioration potentielle sur la recherche et la trajectoire de dérive de cibles maritimes classiques. Basé sur ces résultats, une méthode spécifique d’assimilation automatiquement de telles données dans les outils opérationnels de recherche et de sauvetage devrait être explorée. Choisnard et al. 11 Introductio Currently, there are several C-band synthetic aperture radar (SAR) satellites that can be used to derive surface winds, including ERS-2, RADARSAT-1, and ENVISAT (advanced SAR instrument or ASAR). The latter two satellites provide the best coverage (500 km), since both have modes with a relatively wide swath to increase revisit frequency to a given geographical location. The big advantage of SAR data is the proven ability to derive higher resolution winds than is currently available from traditional sources of meteorological data such as scatterometer or numerical weather prediction. There are several practical perspectives where the highresolution (kilometre or subkilometre scale) wind fields from SAR data offer an important benefit. A variety of possible roles for SAR-derived winds, ranging from weather analysis to climate research and risk management, are described in several papers (Pichel et al., 1999; Johannessen, 2000; Katsaros et al., 2000; Monaldo, 2000; Vachon et al., 2000; Flett et al., 2002; © 2007 Government of Canada 1 Can. J. Remote Sensing, Vol. 33, No. 1, pp. 1–11, 2007 Received 13 January 2006. Accepted 10 December 2006. J. Choisnard,1 D. Power, C. Howell, and C. Randell. C-CORE, Captain Robert A. Bartlett Building, Morrissey Road, St. John’s, NL A1B 3X5, Canada. F. Davidson. Department of Fisheries and Oceans, St. John’s, NL A1C 5X1, Canada. B. Stone. Maritime Search and Rescue, Canadian Coast Guard, St. John’s, NL A1C 5X1, Canada. 1Corresponding author (e-mail: julien.choisnard@ec.gc.ca). Perrie et al., 2002; Monaldo and Kerbaol, 2004; Horstmann et al., 2005). The availability of near real time, calibrated, wide-swath SAR imagery from RADARSAT-1 and ENVISAT ASAR offers the prospect of operational wind products. In this case, this potential has motivated the evaluation of SAR-derived winds for Canadian Coast Guard (CCG) search and rescue operations. The CCG uses the Canadian search and rescue program (CANSARP) software to predict target drift and produce search patterns to task search assets (boats and planes) (Seaconsult Marine Research Ltd., 1993). The CANSARP drift algorithms are linked to archived historical currents and online wind forecasts from the Canadian Meteorological Centre (CMC). The reduction of drift track uncertainty in CANSARP benefits marine search and rescue systems. Potential improvements include the use of additional sources of accurate “nowcasts” and hindcasts of the driving forces (wind and current) to allow more accurate drift forecasts and potentially more efficient search patterns. The requirement for timely availability of accurate high-resolution wind-field and surface-current products over large areas suggests the use of earth observation technology combined with dynamical models and data assimilation. In this case, SAR-derived wind fields were used to improve drift trajectory forecasting of common search and rescue objects, such as persons in the water and life rafts. This paper describes the potential use of SAR satellites for search and rescue drift trajectory forecasting and the initial findings on the use of algorithms to derive surface winds from SAR imagery. First, the SAR data, collocated winds, and search and rescue experiments are described. Three SAR wind vector retrieval methods that use the scatterometry-based approach are evaluated. SAR-derived wind-field products are compared with collocated scatterometer winds to evaluate the most promising approach. The suitability of SAR imagery for use in search and rescue is briefly discussed with respect to expected SAR wind retrieval times and potential availability of SAR over specific areas. Examples of SAR wind fields used with CANSARP simulation are presented to demonstrate drift forecast improvements. In summary, a perspective of future work for the use of SAR winds in search and rescue operations is presented. SAR imagery and collocated data