{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T06:31:48Z","timestamp":1767853908764,"version":"3.49.0"},"reference-count":83,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hellenic Foundation for Research and Innovation","award":["4129"],"award-info":[{"award-number":["4129"]}]},{"name":"Research Committee of the University of Patras","award":["4129"],"award-info":[{"award-number":["4129"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD under cloud-free conditions based on the synergy of machine learning algorithms (MLAs) and ground-based solar irradiance data. The performance of the proposed methodology was investigated by applying different components of solar irradiance. In particular, the use of direct instead of global irradiance as a model feature led to better performance. The MLA-based AODs were compared to reference AERONET retrievals, which encompassed RMSE values between 0.01 and 0.15, regardless of the underlying climate and aerosol environments. Among the MLAs, artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of MLA-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), MAE of 0.01, and RMSE of 0.02. Compared to satellite (MODIS) and reanalysis (MERRA-2 and CAMSRA) data, the MLA-AOD retrievals revealed the highest accuracy at all stations. The ML-AOD retrievals have the potential to expand and complement the AOD information in non-existing timeframes when solar irradiances are available.<\/jats:p>","DOI":"10.3390\/rs16071132","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:28:06Z","timestamp":1711369686000},"page":"1132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements"],"prefix":"10.3390","volume":"16","author":[{"given":"Stavros-Andreas","family":"Logothetis","sequence":"first","affiliation":[{"name":"Laboratory of Atmospheric Physics, Physics Department, University of Patras, GR-26500 Patras, Greece"}]},{"given":"Vasileios","family":"Salamalikis","sequence":"additional","affiliation":[{"name":"NILU\u2014Norwegian Institute for Air Research, P.O. Box 100, 2027 Kjeller, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6612-4991","authenticated-orcid":false,"given":"Andreas","family":"Kazantzidis","sequence":"additional","affiliation":[{"name":"Laboratory of Atmospheric Physics, Physics Department, University of Patras, GR-26500 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"ref_1","unstructured":"Gomis, M.I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J.B.R., Maycock, T.K., Waterfield, T., Yelek\u00e7i, O., Yu, R., and Zhou, B. (2021). IPCC, 2021: Climate Change 2021: The Physical Science Basis, Cambridge University Press. in press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.5194\/acp-19-2965-2019","article-title":"Aerosol optical properties over Europe: An evaluation of the AQMEII Phase 3 simulations against satellite observations","volume":"19","author":"Balzarini","year":"2019","journal-title":"Atmos. Chem. 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