Oil spill mapping and detection represent a relevant issue from an environmental point of view, given the effects on marine ecosystems. This study presents a new feature space assessment protocol for oil spill mapping using the Google Earth Engine (GEE). First, we selected five free Sentinel-1A sensor images from the GEE catalogue. Next, we processed the features evaluated from Gray Level Co-occurrence Matrix (GLCM) spectral and texture data. A recursive protocol that comprises a sequential classification of the evaluated image was also applied, wherein each iteration, the feature with less importance, was removed based on the Gini index. We used the Random Forest algorithm for image classification. Each image was trained on 10,000 points and evaluated for accuracy, with an equal number of points collected independently. Our results showed that the Sum Average (Savg), Convolution Smooth (Smooth), Cluster Shade Shade, and Gray level Correlation (Corr) features were essential to identify oil spills and increase the accuracy values. The best classification results based on the features removal experiment and global accuracy were Angola (0.9960), Trinidad and Tobago (0.9829), Italy (0.9506), Kuwait (0.9547), and Dubai (0.9344). Furthermore, it revealed that the protocol created was essential for better understanding the parameter space to detect oil spills with SAR images.

Oil spill mapping and detection represent a relevant issue from an environmental point of view, given the effects on marine ecosystems. This study presents a new feature space assessment protocol for oil spill mapping using the Google Earth Engine (GEE). First, we selected five free Sentinel-1A sensor images from the GEE catalogue. Next, we processed the features evaluated from Gray Level Co-occurrence Matrix (GLCM) spectral and texture data. A recursive protocol that comprises a sequential classification of the evaluated image was also applied, wherein each iteration, the feature with less importance, was removed based on the Gini index. We used the Random Forest algorithm for image classification. Each image was trained on 10,000 points and evaluated for accuracy, with an equal number of points collected independently. Our results showed that the Sum Average (Savg), Convolution Smooth (Smooth), Cluster Shade Shade, and Gray level Correlation (Corr) features were essential to identify oil spills and increase the accuracy values. The best classification results based on the features removal experiment and global accuracy were Angola (0.9960), Trinidad and Tobago (0.9829), Italy (0.9506), Kuwait (0.9547), and Dubai (0.9344). Furthermore, it revealed that the protocol created was essential for better understanding the parameter space to detect oil spills with SAR images.