###### Abstract  
The purpose of the study is to assess the suitability of synthetic aperture radar (SAR) data to provide sea surface wind (SSW) fields along with a spatial distribution of both SSW speed and direction uncertainty. A simple methodology based on geophysical model function (GMF) inversion to obtain a spatial distribution of both SSW speed and its uncertainty is proposed. Exploiting a dataset of Sentinel-1 images, a sensitivity analysis of the SSW speed uncertainty is carried out on both the uncertainties and the mean values of SAR normalised radar cross section (NRCS), incidence angle and SSW direction, at different spatial scales. The results show that SSW speed uncertainty significantly increases with wind vector cell (WVC) dimension. Moreover, the dominant contribution to the SSW speed uncertainty due to both NRCS and SSW direction uncertainty must always be taken into account. A better precision and accuracy in the estimation of SSW speed and its uncertainty is evidenced by C-band model 7 (CMOD7) GMF rather than the C-band model 5.N (CMOD5.N). To evaluate the results of SSW retrievals, wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model are also exploited for comparisons. Findings indicate a high correlation between the uncertainty from SAR estimations and that from the comparison of SAR vs. ECMWF. s 20222028 March 2022028 March 202231 March 202231 March 202231 March 202231 March 202231 March 202231 March 202231 March 202231 March 202231 March 202231 March 202231 March 20231 March 20231 March 20231 March 20231 March  
and radar altimeters are suitable for synoptic and mesoscale research on the ocean wind. Scatterometers provide very accurate winds in rain-free conditions with a typical wind speed error of about 1 m/s [6], at spatial grids up to 12.5 km (e.g., ASCAT coastal wind) [7]. Radar altimeters show instead a typical error of wind speed of about 1.5 m/s [5,8], however, providing as scatterometers a global coverage but with a low repeat mission period. Global NWP models suffer from the same problems of precision and accuracy in marine coastal areas [9,10], although wind vectors are worldwide provided with good temporal and spatial resolutions (e.g., up to about 25 km resolution for the ECMWF Re-Analysis 5--ERA5 hourly reanalysis). They may not adequately reproduce the spatial variability of the local wind in such areas [11], considering that global estimates are given with a wind speed accuracy of about 2 m/s [12]. Regional NWP models are characterised by higher resolutions in time and space. They are optimised for lower scale dynamics, but they are unfortunately not always available. Furthermore, synthetic aperture radar (SAR) systems have been demonstrated over the last decades to be a powerful instrument for observing the sea surface at high spatial resolution over large areas, hence providing wind speed and direction measurements over the ocean, including marine coastal areas as well. The importance of SAR-derived SSW retrievals is especially recognized, at both the global and local scale, in a wide range of applications such as marine meteorology (e.g., in the study of air-sea interaction [13] or hurricanes [14]), oceanography (e.g., regarding ocean dynamics research of wind-driven sea surface waves and currents [15]), and oil spill monitoring [16]. SAR applications are also used in the research on wind resource assessment [17] and environmental modelling [18]. The conventional way to report the accuracy of SAR SSW maps is through an overall error estimation by considering in situ or NWP models data used as reference. However, summary measures, such as the root mean square error (RMSE), the mean bias error (MBE), the square of the correlation coefficient (R\({}^{2}\)), are not able to represent the spatial variation of the accuracy within the SSW maps obtained from retrieval algorithms.