## 3 Materials and Methods
### SAR Images  
The Synthetic Aperture Radar (SAR) imagery, equipped with dedicated models and analysis tools, can provides information related to the water-surface, such as wind waves, currents, bathymetry, biological activity and precipitation. To first order, ocean SAR backscatter depends on the wind-generated short waves. Therefore, to estimate the wind-related impact on the SAR image is mandatory to interpret features such as the occurrence of phytoplankton bloom, oil spill, current fronts, surface eddies and others. In this purpose, ESA processes and delivers a Level-2 (L2) ocean product (OCN) designed to provide geophysical parameters related to wind, waves and surface velocity to the users. All acquisitions over the Curonia Lagoon are processed up the Level-2. The ocean wind component of the L2 OCN product is available for all acquisition modes. For this study, only the geophysical parameters relied on L2 OCN products acquired in interferometric wide (IW) swath mode are considered. In the L2 OCN product, the normalised radar cross section (NRCS) is used to derive the 10 m ocean surface winds using the geophysical model (GMF) called CMOD-IFR2 [39]. Since SAR is a single fixed antenna instrument, it cannot estimate both wind speed and direction and some a priori wind information is needed. ESA takes the additional wind vector information from the ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric model. To retrieve the ocean surface wind, radar parameters (NRCS values, incidence angle and track angle) and the a priori wind information are combined into a Bayesian scheme, described by [PERSON] et al. [40]. Although SAR wind retrievals are available for VV and HH polarization, only VV related winds are used throughout the paper. Both the a priori ECMWF wind vector and the related SAR retrieved winds are stored in the L2 OCN netcdf files. This wind inversion procedure has been successfully validated and applied to open ocean images. However, less work has been carried out for closed or semi-enclosed water basins, like the Curonia Lagoon, where the geographical boundaries may affect air-sea processes and sea states and wind regimes are controlled by short temporal and spatial fetches. Figure 2a shows the scatter-plot between the _a priori_ wind velocity given by ECMWF, \(\text{w}_{ECMWF}\) and the related SAR retrieved wind velocity, \(\text{w}_{S1}\), for 35 S1 images between 1 May 2019 and 30 June 2019, when no scum events were observed. As the \(\text{w}_{S1}\) is processed from \(\text{w}_{ECMWF}\), these two  
velocities have to clearly be correlated by construction. However, in Figure 2a it is evident that other features can affect SAR backscatter to the first order. In this study we are not interested to investigate all these features but rather to discuss the impact of the presence of phytoplankton blooms on water surface. In [PERSON] et al. [20], it is shown that phytoplankton blooms at water surface acts as a viscous layer by reducing the gravity-capillary waves generation and propagation. From the SAR sensor point of view, scum phenomena are characterized by a reduced backscattered energy. Indeed, a drastic decrease in the NRCS (with attenuation greater than 8-10 dB) for Chl-a concentrations higher than 50 mg/m\({}^{3}\) and wind speeds in the range of 2 to 6 m/s is observed. This NRCS attenuation in turn leads to a retrieved SAR wind speed significantly smaller in respect to the ECMWF a priori guess. The L2 OCN data displayed in Figure 2a refer to a data-set where no phytoplankton bloom (or ice) events have been registered and that is independent from the one considered in Section 4 for results interpretation. Empirically a \"scum alert\" can be raised when the retrieved wind is lower than the worst case observed in a no-scum calibration data-set.