# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 2 Related Work  
For more than a decade, scientists have been trying to use the available satellite data in order to quantify the NO\({}_{2}\) emission from the shipping industry as a whole. For instance, using the measurements from the Global Ozone Monitoring Experiment (GOME) [14] instrument on board the second European Remote Sensing satellite (ERS-2), the authors estimated the NO\({}_{2}\) emission level above the shipping lane between Sri Lanka and Indonesia [15]. With the images from the SCanning Imaging Absorption spectroMeter for Atmospheric CartographY (SCIAMACHY) [16] on board the ENVIronmental SATellite (Envisat) mission, traces from the shipping industry over the Red Sea were quantified [17]. Finally, data from the Ozone Monitoring Instrument (OMI) [18] aboard the NASA Aura spacecraft was used to visualize a ship's NO\({}_{\rm x}\) emission inventory for the Baltic Sea [19]. However, due to the significantly lower resolution capabilities of the above-mentioned predecessors of TROPOMI/S5P (GOME: \(40\times 320\) km\({}^{2}\), SCIAMACHY: \(30\times 60\) km\({}^{2}\), OMI:\(13\times 25\) km\({}^{2}\), TROPOMI: \(3.5\times 5.5\) km\({}^{2}\)), these studies were based on multi-month data averaging, which does not give the possibility to quantify the emission from individual ships. There are many studies demonstrating the capability of TROPOMI NO\({}_{2}\) measurements to pinpoint the emission from urban (e.g., [20; 21; 22]) or industrial sources, such as the mining industry [23] or a gas pipeline [24], as well as showing the possibility of the usage of TROPOMI data to quantify the positive effects of the COVID-19 lockdowns (e.g., [25]). Nonetheless, all studied emission sources are stationary (therefore, can be observed over an extended period of time) and emit much higher quantities of NO\({}_{\text{x}}\) than an individual ship. Thus, all the above-mentioned problems are arguably less complex than the one discussed in this paper. In [11], it was reported that the NO\({}_{2}\) plumes produced by individual ships can be visually distinguished in TROPOMI data. However, since the NO\({}_{2}\) traces of most of the ships in the area are not sufficiently stronger than the background concentrations, only plumes of the largest ships were addressed in that study. In addition, the presented approach requires multiple manual steps, which makes it impossible to apply the method on a global scale. In [12], we introduced the first attempt of a fully automated pipeline for the estimation of NO\({}_{2}\) emission from individual ships. In the study, we showed that pre-processing of the TROPOMI signal allows a visual distinction of a greater amount of ship plumes. The plume-background separation itself, however, was based on a locally optimized threshold, established individually for each of the analyzed ships. Due to the fact that the threshold was established on the basis of the only variable (NO\({}_{2}\) concentration), it was not flexible enough for a good quality of separation of the ship plume from the background. Finally, the one-feature-based method of thresholding does not allow for differentiation of the plume produced by the analyzed ship from all the other NO\({}_{2}\) plumes that might be located in the ship's proximity. Machine learning has proven to be an efficient technique for solving problems in geosciences and remote sensing [26; 27]. Recently emerging studies show the efficiency of applying machine learning models to the TROPOMI data as well. For instance, in [22], the authors built a deep convolution neural network to classify images into those that contain an NO\({}_{2}\) plume from those that do not. The developed model, however, does not differentiate the sources of the detected plumes. Moreover, the study does not provide the attempts of the segmentation of the detected plumes from the background; thus, there is no possibility to quantify the amount of NO\({}_{2}\) that was emitted by a given source.