# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## 4 Discussion  
In the framework of oil spill detection by satellite techniques based on VNIR data, the occurrence of sun glint conditions can be seen as an added value that is helpful for the discrimination between oil slicks and the surrounding water for almost all the techniques developed so far [5; 15; 17; 23; 46; 47; 48]. Techniques that work in such conditions strongly depend on the use of ancillary data (e.g., wind speed, aerosol characterization), which are often site-related and not always available, which limits their accuracy as well as their exportability. On the other hand, as widely explained in Section 2.3, the previously mentioned added value is not a favorable element for multi-temporal techniques based on historical signal variation analysis. Therefore, a methodology, like the one presented in this case, which is able to automatically manage images acquired under peculiar observation conditions by exploiting only satellite data, can guarantee a clear advantage, especially for operational and real-time applications. The unavailability of all the information/data necessary for implementing other optical techniques prevented a direct assessment of the results achieved in this work. Therefore, the results presented in this paper were assessed by comparing with the outputs obtained independently by another methodology based on active microwave data. The Textural Classifier Neural Network Algorithm (TCNNA) methodology [44] was developed for SAR-Envisat data to map the spill resulting from DWH platform explosion. The algorithm simultaneously processes SAR images and ancillary wind data (obtained as outputs from the geophysical model CMOD5) [44; 49] by using a combination of two Neural Networks (NN). The first NN identifies pixels potentially affected by oil when they become the input to the second NN, which performs a statistical textural analysis to definitively discriminate floating oil [19]. The final product is a map of the affected spill area, which can be downloaded as a shape file from the Environmental Response Management Application (ERMA) web application [50]. SAR data used by the TCNNA methodology have different acquisition times compared to MODIS scenes used in this study. To assess our results, two shape files were downloaded from the ERMA website: one relative to the TCNNA result produced by analyzing the TerraSAR image from 25 April 2010 at 11:50 GMT (acquired about 7 h earlier than the MODIS Aqua one) and another one generated using the Envisat data from 29 April 2010 at 03:45 GMT (acquired 13 h earlier than the MODIS Terra one) (Figure 14). Figure 13: (**a**) MODIS Aqua image from 27 May 2010 at 18:55 at 0.8 \(\upmu\)m. (**b**) Reflectance trend along the Y-Y\({}^{\prime}\) transect compared with mean and mean \(\pm\) sigma trends. The comparison between the two methodologies is shown in Figure 15 where pixels detected by the new RST-OIL approach possibly affected by oil are highlighted in green (considering \(\theta_{\text{R50.8}}>2\)), whereas the TCNNA spilled areas are depicted in violet. The overlapping of the two maps shows that -99% of the pixels detected by the methodology proposed in this work are within the oil-affected areas and are identified by TCNNA, which confirms the high reliability of RST-OIL for effectively detecting the presence of an oil spill. Focusing on Figure 15a, in which the temporal gap between the two images is lower and the matching is more accurate, we found that TCNNA detected about 1100 km\({}^{2}\) of oil (area within violet line), of which RST-OIL identified about 700 km\({}^{2}\) (green pixels), which left about 36% of the oil area undetected. The difference in terms of detected area extension may have been due to some factors, such as (i) the different spatial resolution between the data used, with values of about 20 m and 150 m for TerraSAR and SAR-Envisat, respectively, against the 250 m relative to the MODIS sensor, and (ii) the time gap between microwave and optical data acquisition time (7 h for TerraSAR and 13 h for Envisat, respectively).