Supervised segmentation of NO2 plumes from individual ships using TROPOMI satellite data

TL;DR

This paper introduces a supervised machine learning method for automated segmentation of NO2 plumes from ships using TROPOMI satellite data, significantly improving detection accuracy and aiding global emission monitoring.

Contribution

The study presents a novel supervised segmentation approach that outperforms previous methods and demonstrates high correlation with emission proxies, advancing remote sensing-based ship emission monitoring.

Findings

Over 20% increase in average precision score compared to previous methods.

High correlation of 0.834 with ship emission proxy.

Effective automated segmentation of NO2 plumes from satellite data.

Abstract

The shipping industry is one of the strongest anthropogenic emitters of $\text{NO}_\text{x}$ -- substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the $\text{NO}_\text{2}$ plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/S5P). To deploy a remote sensing-based global emission monitoring system, an automated procedure for the estimation of $\text{NO}_\text{2}$ emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data as well as the absence of ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of $\text{NO}_\text{2}$ plumes produced by seagoing ships using supervised machine learning on TROPOMI/S5P data. We show that the proposed approach leads to a more than a 20\% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step toward the development of an automated procedure for global ship emission monitoring using remote sensing data.