# A MODIS-Based Robust Satellite Technique (RST) for Timely Detection of Oil Spilled Areas
## 2 Materials and Methods
Flowchart depicted in Figure 2 provides a summary of the different steps implemented to produce the reference fields. Firstly, all the available MODIS-Aqua images of the month of June (from 2002 to 2010) acquired over the area affected by the event in the 9-12 p.m. GMT temporal range were collected. In detail, about 250 MODIS-Aqua data (level 0), downloaded from NASA ocean color web site [48], were converted to calibrated radiances using SeaDAS V7.3. It is worth nothing that the number of the investigated images (i.e., 250) is higher than the one found as minimum value (i.e., 80) to be representative of the investigated long-term time-series signal by an independent study applied on RST approach [49]. Then R\({}_{\mathrm{rc}}\) data were derived for MODIS bands 1-2 and mapped to a WGS84 Lat/Long projection, producing a subset sized 800 \(\times\) 800 and centred at 34\({}^{\circ}\)N 32.5\({}^{\circ}\)E (e.g., Figure 3) for each pass. Secondly, starting from this long-term R\({}_{\mathrm{rc}}\) series, temporal average (\(\mu_{\mathrm{px}}\)(x,y)) and standard deviation (\(\sigma_{\mathrm{px}}\)(x,y)) reference fields were computed at a pixel level over this area for MODIS channels 1 and 2. Before finalizing such a procedure, a cloud screening test was applied to discharge any cloudy-affected pixel. Cloud-free satellite records were selected by implementing the One-channel Cloud-detection Approach (OCA) [50; 51] using, within an RST-based scheme, the combination of the signal measured in MODIS channels 1 and 32 (11.770-12.270 \(\upmu\)m) at a spatial resolution of 1 km and then downscaled to 250 m. In detail, pixels showing at the same time statistically high reflectance values \"AND\" (i.e., as a logical operator) low brightness temperatures were considered as cloudy affected. Furthermore, a \(k\sigma\) iterative procedure was implemented at a pixel level to remove possible outliers (known or not) from the investigated series [52]. At the end of this phase, four reference fields (one temporal mean + one standard deviation x two channels) were produced for the month of June. Among them, \(\mu_{\mathrm{px}}\)(x,y) represents the expected value (i.e., in unperturbed conditions) at pixel level of the investigated signal, while \(\sigma_{\mathrm{px}}\)(x,y) describes its natural variability (i.e., the statistical dispersion of the data set values around the mean). Hence \(\otimes_{\mathrm{px}}\)(x,y,t), as computed in Equation (1), is a measure of how much the signal deviates from its normal behaviour taking also into account its variability. Such an index is, for construction, a standardized variable which should show a Gaussian behavior (i.e., mean = 0 and standard deviation = 1). Therefore, the higher the absolute value measured, the lower the probability of occurrence. Namely, the probability decreases from about 2.5% for \(\mid\otimes_{\mathrm{px}}\mid>\) 2 to 1.5% for \(\mid\otimes_{\mathrm{px}}\mid>\) 3, and even lower values for increasing \(\mid\otimes_{\mathrm{px}}\mid\) values [53] in case of a perfect Gaussian distribution. Therefore, in the presence of oil, which should represent a perturbation of the normal seawater condition, such an index will show high positive values in case of a positive contrast and the opposite (i.e., high negative) in case of a negative one.