# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 6 Discussion  
The presented approach opens new perspectives for the application of remote sensing in the domain of ship emission monitoring. However, there are several points on the generalization of the results, the methodology, and the TROPOMI detection limit we would like to address here. Firstly, we would like to discuss the possibility of the application of the proposed methodology to other regions. In this study, we presented a general approach that allows for the application of machine learning models for more efficient, automated segmentation of plumes from individual ships using TROPOMI data. All steps of feature preparation can be performed on the data from any region of the globe. Nevertheless, the machine learning models will have to be retrained on the region-specific datasets. Secondly, not all regions will be equally suitable for the performance of ship emission monitoring with remote sensing. In particular, at the moment, there is no scientific evidence that, under the thick layer of land-based emission outflow, it will still be possible to differentiate plumes produced by ships. Therefore, areas that lie in close proximity to big cities, ports, or industrial objects are currently challenging to analyze. The next point is the validation approaches used in this study. For the training of the machine learning model, we used human labels. Human labeling is the basis of all machine learning methods, and this study pioneers ship plume segmentation with more efficient supervised learning based on human labeling. However, the dispersion and chemical transformation of a ship plume, as well as its non-rigid structure mean that there are always some parts of this plume that are at or beyond the visible detection limit of the combination of the TROPOMI instrument and the retrieval algorithm. This can cause errors in labeling, as is demonstrated in Figure 15. Such mistakes if present in reasonable amounts should not affect the performance of the model, but, if the number of labeling errors is too high, the machine learning model will not be able to learn properly and, thus, the resulting performance will be very poor. The fact that nonlinear models were able to easily outperform thresholding-based benchmarks suggests that the models were able to use the provided labels for training, and thus, the labeling error rate was low. Nevertheless, an independent measure of the method evaluation is needed. Since the interest of our study centers on seagooing ships, the in situ measurements cannot be considered as a potential way of method validation. The option of on-board measurement of fuel samples cannot be performed at the scale of the study. Therefore, a theoretical measure of ship emission potential, which is the ship emission proxy, turns out to be the only available option of a reference value for the results of this study. The usage of the ship emission proxy, however, has its limitations, namely the used ship emission proxy does not take into account many factors that influence the expected level of emission for a given ship. Nonetheless, the used proxy allowed us to rank the emission potential of the analyzed ships properly. Following this, we would like to discuss the fact that, even though only fast ships were taken into consideration in this study, the number of ships for which the plume was possible to distinguish is higher than the number of ships for which the plume was invisible for the labeler. This study focused on observing emission sources at the edge of the detection limits of the TROPOMI instrument. It is, therefore, likely that, under certain circumstances, ship plumes remain undetected. We can only in part explain under what circumstances plumes are not visible. With the data presented in Figure 17 and Table 5, we show that, as expected, smaller and slower ships are more often not detected. Similarly, for high wind speeds, the detection is more challenging due to the high dilution of the ships' emissions and, therefore, low concentrations (the evidence can also be found in Figure 17 and Table 5).