## 5 Conclusions  
This paper proposes a \"ready to use\" approach to detect cyanobacteria bloom in the Curonian Lagoon based on the dependency between wind vector and radar backscatter. The open source and self-contained database of the level 2 ocean product Sentinel-1 images is considered. This method has been applied for Sentinel-1 images acquired in summer/autumn 2018. Qualitative comparison with optical imagery gathered from Sentinel-2 and Sentinel-3 satellites combined with meteorological data reveals that the method shows reasonable results for most of the analysed cases (the 79%). It presents  
Figure 6: Temporal trends of WR (red bars), Chl-a concentrations (black bars) and Total Precipitation (TP) (gray bars) from July to October 2018. Images with Chl-a \(<\) 72 mg/m\({}^{3}\) or WR \(>\) 0.1 in all the domain are classified as “No BAD” [43]. Total Precipitation, indicated as a grey background when positive, come from ERA5 hourly data on single level reanalysis database and when is greater than zero is indicated as gray background. Temporal scheme is explained in the insert on the left.  
the advantage to be an easy and self-sustained approach able to provide observations independently on the presence of atmospheric haze or cloud cover.  
The presented findings strongly support the use of L2 S1 products to improve the spatio-temporal detection of algal bloom in Curonian Lagoon and complete the observations from optical sensors. Nonetheless, due to the unavailability of coincident pairs of SAR and optical imagery, a dedicated field observations could however refine the wind ratio threshold empirically determined in this paper. Overall, the combination of the three Sentinels (1, 2 and 3) is providing unprecedented observations in frequency and availability to water ecosystems studies concerned with trends and episodic events. The possibility of having multiple spatial scales (from 10 of S2 to 1 km of S1 L2 products) with different sensing time (from the earlier S3 to the later S1) finally support the understanding of cascade effect processes (e.g., cyanobacterial growth with scum formation), characterized by high degree of change and across a range of scales.  
Conceptualization, M.B., G.D.C. and F.D.S. methodology, F.D.S., G.L., M.B., C.G., F.P.L., G.P., D.V. and G.D.C.; writing--original draft preparation, F.D.S.; writing--review and editing, F.D.S., G.L., M.B., C.G., F.P.L., G.P., D.V. and G.D.C.; supervision, M.B., G.D.C. and C.G.  
This work was supported by the EU Horizon 2020 programme through the EOMORES project [grant number 730066]. The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
S1 Sentinel-1 S2 Sentinel-2 S3 Sentinel-3 L2 Level 2 product for Sentinel-1 SAR Synthetic Aperture Radar AVHR Advanced Very High Resolution Radiometer ERS-1 European Remote-Sensing satellite MERIS MEdium Resolution Imaging Spectrometer ASAR Advanced Synthetic Aperture Radar (ASAR) L2 Level 2 product for Sentinel-1 MODIS Moderate Resolution Imaging Spectroradiometer MSI Multispectral Instrument OLCI Ocean and Land Colour Instrument Chl-a Chlorophyll-a concentration 6 SV Second Simulation of the Satellite Signal in the Solar Spectrum-Vector code AOT Aerosol Optical Thickness NIR Near-InfraRed reflectance L2 OCN Level-2 (L2) Ocean product (for S1) NRCS Normalised Radar Cross Section ECMWF European Centre for medium-range weather forecasts