## 4 Results
### Application Example  
An example of how the use of the proposed index could enrich a temporal characterization of the lagoon is shown in Figure 6. Let us recall that the main goal of the SAR based analysis here proposed consists in fill the gaps leaved by optical remote sensing techniques. This can be the case of no acquisition, of cloudy sky condition and of afternoon hours. As done in previous sub-section, we have thus to investigate whether wind ratio classification is compatible with the optical band ratio ones. Given the the large geographic extension of the lagoon and the wide window in which phytoplankton blooms can occurs, that is, almost 4 months, we propose hereafter the simplest and most synthetic approach for a temporal characterization of the lagoon.  
Following the Water Framework Directive 2000/60/EC [43], the simplest temporal characterization of the lagoon can be performed classifying as \"No BAD\" images where Chl-a \(<\) 72 mg/m\({}^{3}\) in all the domain and as \"BAD\" any other. According to this paper's hypothesis and the results showed above, the equivalent classification for S1 images corresponds to \"No BAD\" if WR \(>\) 0.1 everywhere in the lagoon and \"BAD\" otherwise.  
Considering that C band radar can be additionally influenced by rain, the total precipitations time series is also showed for completeness. ERA5 hourly data on single level reanalysis database provides the total precipitation time series, represented in Figure 6 as gray background when is greater than zero. It seems that WR index based classification never gives false alarm caused by rainfalls. Indeed most of the days with precipitation and S1 acquisition are classified as \"NO BAD\" and the remaining cases are nearby other \"BAD\" classified images. This confirms that the WR index approach is actually self-sustained and that other meteorologic parameters are not needed.  
Time series in Figure 6 again highlights that there would be a a significant lack of information due to the cloud cover if considering only optical data. Indeed, in this case there will be no data for the 62.2% of the days considered, of which the 67% can be recovered by integration through S1 results. Moreover even when optical data are available, SAR results can be used to evaluate the bloom persistence, as it happens for example on 08/29 and on 10/17.  
Overall there are 28 days in which optical and SAR images are both acquired. Among these, in 16 days optical and SAR classify in the same way the lagoon; for the remaining 12 days some hypothesis can be formulated by distinguishing three different cases.  
* SAR acquisition around 05:00 UTC classified as \"No BAD\" and optical images classified as \"BAD\", this is the case of the 08/24, 09/18, 10/06 and 10/12. WR index seems still working reasonably since bloom typically forms later in the morning as the air temperature increases [41].
* Optical images classified as \"BAD\" and SAR acquisition around 16:00 classified as \"No BAD\".
* for the days 07/24, 08/23, 09/03, 10/15, 10/21 and 10/28 we observe that \(w_{\text{ECMWF}}>\) 6 m/s. Since the threshold wind speeds required for vertical mixing in shallow inland lakes typically goes from 3.1 m/s [44] to 4 m/s [45], it is therefore reasonable to assume that wind can produce shear forces on the water surface able to destabilize cyanobacteria's buoyancy.
* For the remaining two days (08/16 and 09/09) additional information is missing and it is difficult to understand if the WR index is failing or if bloom actually disappears for some unknown reason.  
Summarizing, the SAR based approach returns a lagoon's classification consistent with optical images results and meteorological data (high wind speed) for 22 cases, giving an agreement rate of the 79%. The different classification can the explained by the temporal acquisition gap for the 14% of the cases and solely the remaining 7% show a possible disagreement.