{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T14:09:57Z","timestamp":1773065397194,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Region Ile-de-France in the framework of the Domaine d\u2019Int\u00e9r\u00eat Majeur R\u00e9seau de recherche Qualit\u00e9 de l\u2019air en Ile-de-France (DIM QI2)"},{"name":"Centre National d\u2019\u00c9tudes Spatiales (CNES)"},{"name":"Centre National de Recherche Scientifique\u2014Institute National de Sciences de l\u2019Univers (CNRS-INSU)"},{"name":"Universit\u00e9 Paris Est Cr\u00e9teil (UPEC)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We present a supervised machine learning (ML) approach to improve the accuracy of the regional horizontal distribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry transport model over North Africa and the Arabian Peninsula using Moderate Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. Our method produces daily AOD maps with enhanced precision and full spatial domain coverage, which is particularly relevant for regions with a high aerosol abundance, such as the Sahara Desert, where there is a dramatic lack of ground-based measurements for validating chemistry transport simulations. We use satellite observations and some geophysical variables to train four popular regression models, namely multiple linear regression (MLR), random forests (RF), gradient boosting (XGB), and artificial neural networks (NN). We evaluate their performances against satellite and independent ground-based AOD observations. The results indicate that all models perform similarly, with RF exhibiting fewer spatial artifacts. While the regression slightly overcorrects extreme AODs, it remarkably reduces biases and absolute errors and significantly improves linear correlations with respect to the independent observations. We analyze a case study to illustrate the importance of the geophysical input variables and demonstrate the regional significance of some of them.<\/jats:p>","DOI":"10.3390\/rs15061510","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T01:31:41Z","timestamp":1678411901000},"page":"1510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1958-7499","authenticated-orcid":false,"given":"Farouk","family":"Lemmouchi","sequence":"first","affiliation":[{"name":"Univ. Paris Est Creteil and Universit\u00e9 Paris Cit\u00e9, CNRS, LISA, F-94010 Cr\u00e9teil, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9330-6401","authenticated-orcid":false,"given":"Juan","family":"Cuesta","sequence":"additional","affiliation":[{"name":"Univ. Paris Est Creteil and Universit\u00e9 Paris Cit\u00e9, CNRS, LISA, F-94010 Cr\u00e9teil, France"}]},{"given":"Mathieu","family":"Lachatre","sequence":"additional","affiliation":[{"name":"ARIA Technologies, F-92100 Boulogne-Billancourt, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0634-1482","authenticated-orcid":false,"given":"Julien","family":"Brajard","sequence":"additional","affiliation":[{"name":"Nansen Environmental and Remote Sensing Center (NERSC), N-5007 Bergen, Norway"}]},{"given":"Adriana","family":"Coman","sequence":"additional","affiliation":[{"name":"Univ. Paris Est Creteil and Universit\u00e9 Paris Cit\u00e9, CNRS, LISA, F-94010 Cr\u00e9teil, France"}]},{"given":"Matthias","family":"Beekmann","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Paris Cit\u00e9 and Univ. Paris Est Creteil, CNRS, LISA, F-75013 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3272-8288","authenticated-orcid":false,"given":"Claude","family":"Derognat","sequence":"additional","affiliation":[{"name":"ARIA Technologies, F-92100 Boulogne-Billancourt, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1007\/s00382-019-04788-z","article-title":"Direct and Semi-Direct Radiative Effect of North African Dust in Present and Future Regional Climate Simulations","volume":"53","author":"Tsikerdekis","year":"2019","journal-title":"Clim. 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