# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 4 Results
### Validation with Emission Proxy  
Figure 14 provides the correlation plots of NO\({}_{2}\) values estimated for a given ship on a given day based on the segmentation results of a given model and the theoretically derived NO\({}_{x}\) ship emission proxy \(E_{s}\). Table 4 gives information on the achieved level of Pearson correlation and the number of plumes that were segmented by a certain model. Here, our baseline result is the level of Pearson correlation and the number of plumes that were identified by manual labeling. We can see that the majority of the models detected more plumes than the labeler. However, in all cases apart from XGBoost, the higher number of segmented plumes caused the decrement of the correlation score. The XGBoost model, on the other hand, was able to detect more plumes than the manual labeler, while achieving the highest correlation score. Such a result allowed us to form a hypothesis that the developed machine-learning-based methodology is able to segment plumes better than a human labeler. An example of a case where the XGBoost classifier identifies a plume better than the human labeler can be found in Figure 15. More experiments are, however, required in order to make final conclusions. Figure 14: Pearson correlations between estimated (based on classification results) values of NO\({}_{2}\) emitted by each ship on a given day and a theoretical ship emission proxy. Black lines indicate a fitted linear trend. Grey lines show 30% deviations from the fitted linear trend. Figure 13: Coefficients of the features in the decision function of the linear models and the impurity-based feature importance values for tree-based models. The highest contrast between the scores of the performance metrics and the correlation with the emission proxy can be noted for the NO\({}_{2}\) threshold benchmark model. This is due to the fact that the ship plumes composed out of one pixel in our dataset were not labeled as plumes. The substantially high correlation with the emission proxy suggests that the single-pixel plumes were, nevertheless, identified by the method correctly. An illustration of such an example is provided in Figure 16.  
Figure 16: NO\({}_{2}\)-based thresholding allows for distinguishing plumes cumulated within one pixel of the TROPOMI image. (**a**) TROPOMI NO\({}_{2}\) tropospheric vertical column density. Units: mol/m\({}^{2}\). (**b**) TROPOMI NO\({}_{2}\) image enhanced by [PERSON]’s \(I\). At the top left of the _ship sector_ can be found an example when a cluster of low-value NO\({}_{2}\) was enhanced incorrectly. (**c**) Results of the segmentation of the _NO\({}_{2}\) threshold_ method. A black pixel is a pixel that was identified by a model as a plume. (**d**) Human labels. The absence of black pixels means that there were no pixels within the area labeled as a plume. For all presented images, the size of the pixel is equal to 4.2 \(\times\) 5 km\({}^{2}\). Measurement date: 9 June 2019. Ship type: tanker. Ship length: 285 m. Average speed within the studied time scope: 15.4 kt. Figure 15: XGBoost classifier allows for the segmentation of plumes that were not recognized by the labeler. (**a**) TROPOMI NO\({}_{2}\) tropospheric vertical column density. Units: mol/m\({}^{2}\). The variable was a part of the input to machine learning models. The ship plume is difficult to distinguish by the human eye. (**b**) TROPOMI NO\({}_{2}\) image enhanced by Moran’s \(I\). The variable was a part of the input to machine learning models. After enhancement, the ship plume can be recognized better. At the top of the _ship sector_ can be found an example when a cluster of low-value NO\({}_{2}\) was enhanced incorrectly. (**c**) Results of the segmentation of the XGBoost model. Black pixels indicate pixels classified by the model as a “plume”. (**d**) Human labels. The absence of black pixels means that there were no pixels within the area labeled as a plume. For all presented images, the size of the pixel is equal to 4.2 \(\times\) 5 km\({}^{2}\). Measurement date: 24 June 2019. Ship type: tanker.