{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:17:06Z","timestamp":1767183426260,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,5]],"date-time":"2023-02-05T00:00:00Z","timestamp":1675555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["820852"],"award-info":[{"award-number":["820852"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 \u00b5m). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 \u00b1 0.40 \u00d7 1011 kg (MLE method) and 1.32 \u00b1 0.47 \u00d7 1011 kg (NN method) for Kelud; 4.48 \u00b1 1.61 \u00d7 1010 kg (MLE method) and 4.32 \u00b1 1.56 \u00d7 1010 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.<\/jats:p>","DOI":"10.3390\/rs15040888","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T05:29:05Z","timestamp":1675661345000},"page":"888","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9182-5109","authenticated-orcid":false,"given":"Francesco","family":"Romeo","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Elettronica e Telecomunicazioni, Sapienza Universit\u00e0 di Roma, 00185 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0303-1171","authenticated-orcid":false,"given":"Luigi","family":"Mereu","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, 40128 Bologna, Italy"},{"name":"Centre of Excellence CETEMPS, University of L\u2019Aquila, 67100 L\u2019Aquila, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8704-8629","authenticated-orcid":false,"given":"Simona","family":"Scollo","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, 95100 Catania, Italy"}]},{"given":"Mario","family":"Papa","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Elettronica e Telecomunicazioni, Sapienza Universit\u00e0 di Roma, 00185 Roma, Italy"},{"name":"Centre of Excellence CETEMPS, University of L\u2019Aquila, 67100 L\u2019Aquila, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9432-3246","authenticated-orcid":false,"given":"Stefano","family":"Corradini","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6910-8800","authenticated-orcid":false,"given":"Luca","family":"Merucci","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5873-204X","authenticated-orcid":false,"given":"Frank Silvio","family":"Marzano","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Elettronica e Telecomunicazioni, Sapienza Universit\u00e0 di Roma, 00185 Roma, Italy"},{"name":"Centre of Excellence CETEMPS, University of L\u2019Aquila, 67100 L\u2019Aquila, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2597","DOI":"10.1109\/TGRS.2017.2769003","article-title":"Multi-satellite Multi-sensor Observations of a Sub-Plinian Volcanic Eruption: The 2015 Calbuco Explosive Event in Chile","volume":"56","author":"Marzano","year":"2018","journal-title":"IEEE Trans. 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