{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:17:35Z","timestamp":1770743855843,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Netherlands Human Environment and Transport Inspectorate","award":["814893"],"award-info":[{"award-number":["814893"]}]},{"name":"Dutch Ministry of Infrastructure and Water Management","award":["814893"],"award-info":[{"award-number":["814893"]}]},{"name":"European Union\u2019s Horizon","award":["814893"],"award-info":[{"award-number":["814893"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The shipping industry is one of the strongest anthropogenic emitters of NOx\u2014a 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 NO2 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 NO2 emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data, as well as the absence of the ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of NO2 plumes produced by seagoing ships using supervised machine learning on TROPOMI\/S5P data. We show that the proposed approach leads to 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 towards the development of an automated procedure for global ship emission monitoring using remote sensing data.<\/jats:p>","DOI":"10.3390\/rs14225809","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T04:08:40Z","timestamp":1668744520000},"page":"5809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Supervised Segmentation of NO2 Plumes from Individual Ships Using TROPOMI Satellite Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0202-1898","authenticated-orcid":false,"given":"Solomiia","family":"Kurchaba","sequence":"first","affiliation":[{"name":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, 2333 CA Leiden, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jasper","family":"van Vliet","sequence":"additional","affiliation":[{"name":"Human Environment and Transport Inspectorate (ILT), 3531 AH Utrecht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fons J.","family":"Verbeek","sequence":"additional","affiliation":[{"name":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, 2333 CA Leiden, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacqueline J.","family":"Meulman","sequence":"additional","affiliation":[{"name":"Mathematical Institute, Leiden University, 2333 CA Leiden, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2645-1198","authenticated-orcid":false,"given":"Cor J.","family":"Veenman","sequence":"additional","affiliation":[{"name":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, 2333 CA Leiden, The Netherlands"},{"name":"Data Science Department, TNO, 2595 DA The Hague, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.5194\/essd-10-1987-2018","article-title":"Gridded emissions of air pollutants for the period 1970\u20132012 within EDGAR v4. 3.2","volume":"10","author":"Crippa","year":"2018","journal-title":"Earth Syst. 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