# A MODIS-Based Robust Satellite Technique (RST) for Timely Detection of Oil Spilled Areas
## 4 Conclusions  
Exploiting the intrinsic exportability of the RST approach to any satellite data, in this paper we applied it to MODIS records for the detection and the mapping of oil spills. In particular, higher-spatial-resolution MODIS data (250 m) in the first two channels have been used. The results obtained show, also in comparison with those achieved by implementing another MODIS-based independent method, the feasibility of such an attempt. Anomalies with a very high level of confidence were detected using both data acquired in MODIS channels 1 and 2 in correspondence to a positive contrast between the reflectance of the oil spill and seawater. Such a feature allows us to map the oil slick extension and structure (i.e., the relative thickness) accurately. In addition, the reliability of the proposed approach was further assessed by means of a confutation analysis, which indicated the absence of any \"false\"-positive anomalous signal when no oil spill was likely to be present. Although further studies on additional events and under several observational conditions must be carried out to better evaluate its actual potential, even in the presence of in situ data which could allow its full assessment, especially concerning its mapping capability, these first outcomes encourage us to continue the research in this field. In particular, the generation of operational and reliable products may represent a useful tool especially for heavy oil-tanker traffic areas to evaluate the amount of oil spilled in such regions, provided that information about slick thickness is also available. Besides, concerning possible future methodological developments, using atmospherically corrected radiances instead of Rrc might improve the accuracy of the achieved results for specific local conditions, but with a decrease in the comparability of the achieved results among different geographical areas.  
In the future, MODIS data could be coupled with those provided by other sensors, such as the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIS) aboard Landsat 8, the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP, as well as the Multi Spectral Imager (MSI) on the Sentinel-2 satellites and the two sensors aboard the Sentinel-3 satellites (i.e., the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR)). These data, offering higher performances in terms of spatial and spectral resolutions than MODIS, will help in further improving the overall relevance of the achieved results. Furthermore, the integration with SAR data will allow the implementation of a multi-sensor system, which, exploiting the specific advantages of each single technology, will be able to guarantee a near-real-time sea monitoring system useful in helping the local authorities with the timely management of oil spill risks.  
This work was carried out in the framework of the OP European Regional Development Fund (ERDF) Basilicata Region 2007-2013 IOnian Sea water quality MOnitoring by Satellite data (IOSMOS) project, and the OP European Social Fund (ESF) Basilicata Region 2007-2013 MOnitoraggio delle acque del mar  
Figure 9: MODIS-Aqua-derived Floating Algae Index (FAI) image for 10:50 a.m. GMT 18 June 2007.  
MEDiterrane mediante DAtti Satellitari (MOMEDAS) project. We would like to thank the NASA OBPG group for providing the MODIS data used in this study.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] wrote the majority of the paper. [PERSON], [PERSON] and [PERSON] implemented RST for oil spill detection on MODIS data. [PERSON] and [PERSON] contributed to the analysis of the achieved results. [PERSON], [PERSON] and [PERSON] contributed to the data analysis. [PERSON] and [PERSON] designed the algorithm.  
**Conflicts of Interest:** The authors declare no conflict of interest.