# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 6 Discussion
Regarding the lower detectability at lower wind speeds that can also be observed in Figure 17, we find some accordance with the findings from [47], where it is described how the wind speed impacts the reflectivity of the sea surface due to the shape of the waves, which in turn influences the sensors' sensitivity. However, this topic needs further study in the satellite retrieval community. To sum up, the method presented in this study is a big step towards automated and global ship emission monitoring with remote sensing and should not be devalued by the above-mentioned limitations. Firstly, one can train a machine learning model per region as commonly done in remote sensing. In addition, the region can serve as a feature of the model itself to make it invariant to geographic locations. Moreover, adding of such variables such as month, solar radiation, or temperature will make the model invariant to the seasonal changes that might be more severe at northern latitudes. Secondly, main ship routes go through both more and less suitable regions for the satellite observations. Thus, a selection of the more convenient regions will still allow us to use our approach for efficient monitoring of the emission levels produced by ships that follow those routes. Moreover, the obtained good results both in terms of segmentation quality and comparison with the emission proxy suggest that the labeling was of substantial quality. The proposed methodology also opens new research directions. For instance, human labeling can be replaced with chemical plume dispersion models, which will further improve the labeling quality and make the proposed methodology even more effective. Finally, the problem of the visibility of ship plumes that have been unrevealed with the presented study, once solved, will give us a great overview of the capabilities of TROPOMI sensors. Conceptualization, S.K., J.v.V. and C.J.V. ; methodology, S.K., J.v.V. and C.J.V. ; software, S.K. ; validation, S.K. ; formal analysis, S.K. ; investigation, S.K. ; resources, S.K., J.v.V. and F.J.V. ; data curation, S.K. and J.v.V. ; writing--original draft preparation, S.K. ; writing--review and editing, C.J.V., J.v.V., J.M. and F.J.V. ; visualization, S.K. ; supervision, C.J.V., J.v.V., F.J.V. and J.J.M. ; project administration, F.J.V. ; funding acquisition, J.v.V. All authors have read and agreed to the published version of the manuscript. This work is funded by the Netherlands Human Environment and Transport Inspectorate, the Dutch Ministry of Infrastructure and Water Management, and the SCIPPER project, which receives funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 814893. Figure 17: Distribution of the dataset features for the images, where there were no visible ship plumes distinguished, and for the images, where there was a visually distinguishable ship plume. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Name** & **No Plume Image** & **Image with a Plume** \\ \hline Wind speed (m/s) & 5.47 \(\pm\) 2.31 & 5.27 \(\pm\) 2.00 \\ Ship speed (kt) & 16.83 \(\pm\) 2.01 & 17.41 \(\pm\) 2.04 \\ Ship length (m) & 279.92 \(\pm\) 86.64 & 303.99 \(\pm\) 82.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average and standard deviation for the dataset features for the images, where there were no visible ship plumes distinguished, and for the images, where there was a visually distinguishable ship plume. Data Availability Statement: The TROPOMI/SSP data are freely available via [https://s5 Phub.copernicus.eu/](https://s5 Phub.copernicus.eu/) 28 November 2022. Starting from the product version upgrade from 1.2.2 to 1.3.0, which took place on 27 March 2019, the ECMWF operational model analyses 10 m wind data for coinciding time are available as a support product in the TROPOMI/SSP data file.