# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## Materials and Methods
### Rst-Oil  
The RST approach is an automatic change-detection scheme based on the analysis of historical series of satellite data for complete signal characterization (spectral, spatial, and temporal) followed by an anomaly detection step implemented by using automatically generated local (i.e., for a specific time and place of observation) self-adaptive thresholds. A full description of the methodology can be found in Reference [35]. In its previous configuration, devoted to the automatic and timely detection of oil spills, namely RST-OIL, it was implemented on Advanced Very High Resolution Radiometer (AVHRR) and MODIS TIR data [27; 28; 36; 37], which demonstrates good performance regardless of the observation and environmental conditions. This enabled the detection of different oil spills that occurred in several areas worldwide. RST-OIL has been recently successfully tested in the VIS and NIR MODIS channels, using the corresponding Rayleigh-corrected reflectance (R\({}_{\text{rc}}\)) data to detect an illicit vessel discharge that occurred off the south coast of Cyprus in June 2007 [38]. As mentioned above, the new configuration proposed in this work is based on MODIS NIR data. Therefore, the Absolutely Local Index of Change of the Environment (ALICE) was implemented as follows. \[\otimes_{R_{\lambda}}(x,y,t)=\frac{\left[R_{\lambda}(x,y,t)-\mu_{R_{\lambda}} (x,y)\right]}{\sigma_{R_{\lambda}}(x,y)} \tag{1}\]  
where \(R_{\lambda}\)(\(x\),\(y\),\(t\)) is the MODIS reflectance at \(\lambda\) = 0.8 \(\mu\)m. \(\mu_{R\lambda}\)(\(x\),\(y\)) and \(\sigma_{R\lambda}\)(\(x\),\(y\)) are the temporal mean and standard deviation signals, respectively (namely reference fields) computed by analyzing a long-term dataset of homogeneous satellite records collected at the pixel location \(x\),\(y\) under the same observation conditions (the same month of the year and the same time of acquisition) of the image under investigation. To make the reference fields actually representative of unperturbed conditions, for each pixel (\(x\),\(y\)) of the study area, an iterative k\(\sigma\)-clipping procedure [35] was implemented to exclude possible outliers [41]. The ALICE index is a standardized variable that can be described through a normal (or Gaussian) distribution with zero mean (\(\mu\)) and variance (\(\sigma\)) equal to 1. Therefore, the probability of occurrence of values above 2\(\sigma\) (i.e., \(R_{\lambda}>\mu_{R\lambda}+2\sigma_{R\lambda}\)) or below \(-2\sigma\) (i.e., \(R_{\lambda}<\mu_{R\lambda}-2\sigma_{R\lambda}\)) is about 2.5% and falls to 0.15% for values above 3\(\sigma\) (or below \(-3\sigma\)). Taking into account the above-described dual behavior of oil in the NIR band, the corresponding index \(\otimes_{\text{R0.8}}\) would show high and positive values (at least \(\otimes_{\text{R0.8}}>2\)) for pixels characterized by a positive oil/seawater contrast and the opposite behavior (at least \(\otimes_{\text{R0.8}}<0\)) in case of a negative contrast. The latter, where the reflectance is limited to positive values, which are very low in the NIR band for unperturbed conditions, is an expected behavior that may, in some cases, result in sensitivity issues or missing detections (i.e., false negatives). In order to avoid false alarms related to the high NIR signals of clouds, the latter have been previously identified and discarded from the ALICE computation. Clouds were recognized by using the One-channel Cloudy-Radiance detection Approach (OCA) [42]. The standard OCA approach involves the combined use of TIR and VIS information to identify unexpected low temperatures or high reflective pixels related to the cloud presence. Unfortunately, in the VIS region, clouds and oil often exhibit similar spectral behavior, and, therefore, it was not possible to use the VIS band here. Hence, the TIR signal was exploited as a unique input to generate a binary cloud mask.