**4. Conclusion**

This work shows that by processing radar power reflectivity values, taken from nadir-looking systems under weather conditions suitable for cleaning operations, thick oil slick thicknesses up to 10 mm can be detected, estimated, and classified. The accuracy of each function is dependent on the selected statistical algorithm. This approach is novel since it provides most of the information that is required for an effective contingency plan at the same time during the early stage of the spill. In addition, this novel approach being implemented on drone platforms is a suitable system for tactical responses needed during contingency plans. Moreover, the new approach aims to complement state-of-the-art techniques, such as satellite-based SAR, by covering calm ocean conditions and low wind speeds scenarios that challenge other techniques for oil spill monitoring. Maximum-likelihood and machine learning statistical algorithms could be used directly to quickly assess the scene. The ANN model shows very low complexity, low power consumption, and high accuracy. This demonstrates the feasibility to apply the approach for onboard processing. Despite all the advancement that is presented, there are still open questions, tasks, and challenges to consider in this field for utilized practical solutions. A further investigation on the algorithmic level is still required to make sure that the performance will not decrease given the variable dynamics that could be introduced in the physical environment. Also, the complexity of all proposed approaches should be studied to check the feasibility of implementing them on hardware platforms for onboard processing. Despite the need for detection, thickness estimation, and classification, tracking the spill is also very important on a small scale, especially for moderate wind speeds. It would be useful to add this functionality as a capability of the drone-based solution by considering the weather conditions and the ocean state to track the spill over time. Finally, the proposed work is a proof of concept and helps take the oil-spill-related research work one step forward toward the development of operational tools for oilspill intervention.
