{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:58:15Z","timestamp":1789865895562,"version":"4.0.1"},"reference-count":88,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["RS-2024-00408922"],"award-info":[{"award-number":["RS-2024-00408922"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010700","name":"National Institute of Environmental Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003562","name":"Ministry of Environment","doi-asserted-by":"publisher","award":["NIER-2025-01-02-055"],"award-info":[{"award-number":["NIER-2025-01-02-055"]}],"id":[{"id":"10.13039\/501100003562","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Science of The Total Environment"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.scitotenv.2025.180535","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:39:25Z","timestamp":1758760765000},"page":"180535","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning"],"prefix":"10.1016","volume":"1002","author":[{"given":"Hyunyoung","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seohui","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungho","family":"Im","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunjin","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jhoon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Min","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.scitotenv.2025.180535_bb0005","doi-asserted-by":"crossref","DOI":"10.3390\/s24051490","article-title":"Improving dust aerosol optical depth (DAOD) retrieval from the GEOKOMPSAT-2A (GK-2A) satellite for daytime and nighttime monitoring","volume":"24","author":"Ahn","year":"2024","journal-title":"Sensors"},{"issue":"27","key":"10.1016\/j.scitotenv.2025.180535_bb0010","doi-asserted-by":"crossref","first-page":"4641","DOI":"10.1016\/j.atmosenv.2011.05.055","article-title":"Monitoring spatio-temporal aerosol patterns over Pakistan based on MODIS, TOMS and MISR satellite data and a HYSPLIT model","volume":"45","author":"Alam","year":"2011","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0015","series-title":"Paper Presented at Proceedings of the AAAI conference on artificial intelligence","article-title":"Tabnet: Attentive interpretable tabular learning","author":"Arik","year":"2021"},{"key":"10.1016\/j.scitotenv.2025.180535_bb0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2022.113426","article-title":"The retrieval of aerosol optical properties based on a random forest machine learning approach: exploration of geostationary satellite images","volume":"286","author":"Bao","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0025","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.atmosenv.2015.04.013","article-title":"Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data","volume":"111","author":"Bibi","year":"2015","journal-title":"Atmos. Environ."},{"issue":"6","key":"10.1016\/j.scitotenv.2025.180535_bb0030","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1021\/jasms.3c00403","article-title":"Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics","volume":"35","author":"Bifarin","year":"2024","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2021.111123","article-title":"Global PM2. 5-attributable health burden from 1990 to 2017: estimates from the global burden of disease study 2017","volume":"197","author":"Bu","year":"2021","journal-title":"Environ. Res."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0040","doi-asserted-by":"crossref","DOI":"10.1080\/15481603.2023.2262836","article-title":"An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud","volume":"60","author":"Cai","year":"2023","journal-title":"GISci. Remote Sens."},{"issue":"10","key":"10.1016\/j.scitotenv.2025.180535_bb0045","doi-asserted-by":"crossref","DOI":"10.3390\/toxics11100813","article-title":"Evaluation and comparison of multi-satellite aerosol optical depth products over East Asia Ocean","volume":"11","author":"Cao","year":"2023","journal-title":"Toxics"},{"key":"10.1016\/j.scitotenv.2025.180535_bb0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.112006","article-title":"Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: artificial neural network method","volume":"249","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2022.159673","article-title":"High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method","volume":"857","author":"Chen","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"14","key":"10.1016\/j.scitotenv.2025.180535_bb0060","doi-asserted-by":"crossref","first-page":"4369","DOI":"10.5194\/amt-17-4369-2024","article-title":"First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia","volume":"17","author":"Cho","year":"2024","journal-title":"Atmos. Meas. Tech."},{"issue":"3","key":"10.1016\/j.scitotenv.2025.180535_bb0065","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.5194\/amt-9-1377-2016","article-title":"GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign","volume":"9","author":"Choi","year":"2016","journal-title":"Atmos. Meas. Tech."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0070","doi-asserted-by":"crossref","first-page":"385","DOI":"10.5194\/amt-11-385-2018","article-title":"GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia","volume":"11","author":"Choi","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2023.121169","article-title":"Retrieval of hourly PM2. 5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II","volume":"323","author":"Choi","year":"2023","journal-title":"Environ. Pollut."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2023.113901","article-title":"Retrieving hourly seamless PM2. 5 concentration across China with physically informed spatiotemporal connection","volume":"301","author":"Ding","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0085","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11207-008-9187-7","article-title":"The high-resolution solar reference spectrum between 250 and 550 nm and its application to measurements with the ozone monitoring instrument","volume":"249","author":"Dobber","year":"2008","journal-title":"Sol. Phys."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0090","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.isprsjprs.2024.04.020","article-title":"First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1\/6 synergistic observations: algorithm development and validation","volume":"211","author":"Dong","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb1000","series-title":"IFS Documentation CY47R3 \u2012 Part I: Observations. Book Chapter in IFS Documentation. No. 1. European Centre for Medium-Range Weather Forecasts (ECMWF)","author":"ECMWF","year":"2021"},{"issue":"2","key":"10.1016\/j.scitotenv.2025.180535_bb0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.gsf.2023.101762","article-title":"An analysis of air pollution associated with the 2023 sand and dust storms over China: aerosol properties and PM10 variability","volume":"15","author":"Filonchyk","year":"2024","journal-title":"Geosci. Front."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0100","doi-asserted-by":"crossref","first-page":"169","DOI":"10.5194\/amt-12-169-2019","article-title":"Advancements in the Aerosol Robotic Network (AERONET) Version 3 database\u2013automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements","volume":"12","author":"Giles","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.111759","article-title":"Ground-based retrievals of aerosol column absorption in the UV spectral region and their implications for GEMS measurements","volume":"245","author":"Go","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2022.119238","article-title":"Mega Asian dust event over China on 27\u201331 March 2021 observed with space-borne instruments and ground-based polarization lidar","volume":"285","author":"He","year":"2022","journal-title":"Atmos. Environ."},{"issue":"D23","key":"10.1016\/j.scitotenv.2025.180535_bb0115","doi-asserted-by":"crossref","first-page":"28003","DOI":"10.1029\/97JD02074","article-title":"Earth surface reflectivity climatology at 340\u2013380 nm from TOMS data","volume":"102","author":"Herman","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"issue":"730","key":"10.1016\/j.scitotenv.2025.180535_bb0120","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. Roy. Meteorol. Soc."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(98)00031-5","article-title":"AERONET\u2014a federated instrument network and data archive for aerosol characterization","volume":"66","author":"Holben","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0130","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jqsrt.2017.01.041","article-title":"An algorithm for hyperspectral remote sensing of aerosols: 2. Information content analysis for aerosol parameters and principal components of surface spectra","volume":"192","author":"Hou","year":"2017","journal-title":"J. Quant. Spectrosc. Radiat. Transfer"},{"key":"10.1016\/j.scitotenv.2025.180535_bb0135","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.jqsrt.2020.107161","article-title":"An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign","volume":"253","author":"Hou","year":"2020","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"issue":"3","key":"10.1016\/j.scitotenv.2025.180535_bb0140","doi-asserted-by":"crossref","DOI":"10.3390\/rs13030344","article-title":"Theoretical uncertainty analysis of satellite retrieved aerosol optical depth associated with surface albedo and aerosol optical properties","volume":"13","author":"Huang","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosres.2023.107067","article-title":"Impact of regional climate patterns on the biomass burning emissions and transport over peninsular Southeast Asia, 2000-2019","volume":"297","author":"Huang","year":"2024","journal-title":"Atmos. Res."},{"issue":"6","key":"10.1016\/j.scitotenv.2025.180535_bb0150","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.7780\/kjrs.2024.40.6.1.28","article-title":"Correlation-based evaluation of GEMS satellite products using environmental and meteorological measurements","volume":"40","author":"Jeong","year":"2024","journal-title":"Korean J. Remote Sens."},{"issue":"24","key":"10.1016\/j.scitotenv.2025.180535_bb0155","doi-asserted-by":"crossref","DOI":"10.1029\/2023GL106806","article-title":"A generalized aerosol algorithm for multi-spectral satellite measurement with physics-informed deep learning method","volume":"50","author":"Jiang","year":"2023","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0160","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.isprsjprs.2021.11.016","article-title":"Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia","volume":"183","author":"Kang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2023.119951","article-title":"Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia","volume":"309","author":"Kang","year":"2023","journal-title":"Atmos. Environ."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0170","doi-asserted-by":"crossref","DOI":"10.1080\/15481603.2024.2436997","article-title":"An explainable AI framework for spatiotemporal risk factor analysis in public health: a case study of cardiovascular mortality in South Korea","volume":"61","author":"Kang","year":"2024","journal-title":"GISci. Remote Sens."},{"issue":"7","key":"10.1016\/j.scitotenv.2025.180535_bb0175","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1021\/acsearthspacechem.0c00140","article-title":"Metal concentrations and source apportionment of PM2. 5 in Chiang Rai and Bangkok, Thailand during a biomass burning season","volume":"4","author":"Kayee","year":"2020","journal-title":"ACS Earth Space Chem."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0180","first-page":"1","article-title":"Retrieval of UV-visible aerosol absorption using AERONET and OMI-MODIS synergy: spatial and temporal variability across major aerosol environments","volume":"2021","author":"Kayetha","year":"2021","journal-title":"Atmos. Meas. Tech. Discuss."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0185","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2013.12.003","article-title":"Improvement of aerosol optical depth retrieval over Hong Kong from a geostationary meteorological satellite using critical reflectance with background optical depth correction","volume":"142","author":"Kim","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0190","series-title":"Paper Presented at AIP Conference Proceedings, AIP Publishing","doi-asserted-by":"crossref","DOI":"10.1063\/5.0183243","article-title":"Implementation of the Yonsei Aerosol retrieval algorithm in the GK-2A\/AMI and FY-4A\/AGRI remote-sensing systems","author":"Kim","year":"2024"},{"issue":"14","key":"10.1016\/j.scitotenv.2025.180535_bb0195","doi-asserted-by":"crossref","first-page":"4317","DOI":"10.5194\/amt-17-4317-2024","article-title":"Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods","volume":"17","author":"Kim","year":"2024","journal-title":"Atmos. Meas. Tech."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0200","series-title":"Analysis and Applications of the Geostationary Environment Monitoring Spectrometer (GEMS) Data, Paper Presented at Remote Sensing of Clouds and the Atmosphere XXVIII, SPIE","author":"Lee","year":"2023"},{"issue":"5","key":"10.1016\/j.scitotenv.2025.180535_bb0205","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1109\/TGRS.2006.872333","article-title":"The ozone monitoring instrument","volume":"44","author":"Levelt","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0210","series-title":"Algorithm for Remote Sensing of Tropospheric Aerosol Over Dark Targets From MODIS: Collections 005 and 051: Revision 2; Feb 2009, MODIS Algorithm Theoretical Basis Document","author":"Levy","year":"2009"},{"issue":"11","key":"10.1016\/j.scitotenv.2025.180535_bb0215","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.5194\/amt-6-2989-2013","article-title":"The collection 6 MODIS aerosol products over land and ocean","volume":"6","author":"Levy","year":"2013","journal-title":"Atmos. Meas. Tech."},{"issue":"4","key":"10.1016\/j.scitotenv.2025.180535_bb0220","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1175\/BAMS-D-17-0133.1","article-title":"Comprehensive study of optical, physical, chemical, and radiative properties of total columnar atmospheric aerosols over China: an overview of Sun\u2013Sky Radiometer Observation Network (SONET) measurements","volume":"99","author":"Li","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"issue":"20","key":"10.1016\/j.scitotenv.2025.180535_bb0225","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.3390\/rs12203296","article-title":"Aerosol Direct radiative effects over china based on long-term observations within the sun\u2013sky radiometer observation network (SONET)","volume":"12","author":"Li","year":"2020","journal-title":"Remote Sens"},{"key":"10.1016\/j.scitotenv.2025.180535_bb0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosres.2023.107195","article-title":"Multi-faceted analysis of dust storm from satellite imagery, ground station, and model simulations, a study in China","volume":"299","author":"Li","year":"2024","journal-title":"Atmos. Res."},{"issue":"5","key":"10.1016\/j.scitotenv.2025.180535_bb0235","doi-asserted-by":"crossref","DOI":"10.3390\/rs14051053","article-title":"Estimation of aerosol optical depth at 30 m resolution using Landsat imagery and machine learning","volume":"14","author":"Liang","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0240","doi-asserted-by":"crossref","DOI":"10.3390\/rs10050699","article-title":"AHI\/Himawari-8 Yonsei aerosol retrieval (YAER): algorithm, validation and merged products","volume":"10","author":"Lim","year":"2018","journal-title":"Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0245","doi-asserted-by":"crossref","first-page":"120440","DOI":"10.1016\/j.atmosenv.2024.120440","article-title":"A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data","volume":"325","author":"Liu","year":"2024","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0250","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"10","key":"10.1016\/j.scitotenv.2025.180535_bb0255","doi-asserted-by":"crossref","first-page":"5741","DOI":"10.5194\/amt-11-5741-2018","article-title":"MODIS collection 6 MAIAC algorithm","volume":"11","author":"Lyapustin","year":"2018","journal-title":"Atmos. Meas. Tech."},{"issue":"11","key":"10.1016\/j.scitotenv.2025.180535_bb0260","doi-asserted-by":"crossref","first-page":"9323","DOI":"10.1109\/TGRS.2019.2926110","article-title":"A data-driven approach for accurate rainfall prediction","volume":"57","author":"Manandhar","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"10.1016\/j.scitotenv.2025.180535_bb0265","doi-asserted-by":"crossref","first-page":"6017","DOI":"10.5194\/amt-12-6017-2019","article-title":"Neural network for aerosol retrieval from hyperspectral imagery","volume":"12","author":"Mauceri","year":"2019","journal-title":"Atmos. Meas. Tech."},{"issue":"9","key":"10.1016\/j.scitotenv.2025.180535_bb0270","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-land: a state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Mu\u00f1oz-Sabater","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"issue":"3","key":"10.1016\/j.scitotenv.2025.180535_bb0275","doi-asserted-by":"crossref","DOI":"10.1007\/s12040-024-02387-z","article-title":"Contrasting effects of aerosols on surface temperature over the Indo-Gangetic Plain and Tibetan Plateau","volume":"133","author":"Nair","year":"2024","journal-title":"J. Earth Syst. Sci."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0280","author":"NIER (National Institute of Environmental Research)"},{"issue":"6","key":"10.1016\/j.scitotenv.2025.180535_bb0285","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1164\/rccm.201804-0673PP","article-title":"Air pollution in the Asia-Pacific region. A joint Asian Pacific Society of Respirology\/American Thoracic Society perspective","volume":"199","author":"North","year":"2019","journal-title":"Am. J. Respir. Crit. Care Med."},{"issue":"4","key":"10.1016\/j.scitotenv.2025.180535_bb0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.apr.2024.102054","article-title":"Enhancing ozone nowcasting over East Asia using a data-to-data translation approach with observations from a geostationary environment monitoring spectrometer","volume":"15","author":"Oh","year":"2024","journal-title":"Atmos. Pollut. Res."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0295","first-page":"1","article-title":"Spatiotemporal changes in aerosol properties by hygroscopic growth and impacts on radiative forcing and heating rates during DISCOVER-AQ 2011","volume":"2021","author":"P\u00e9rez-Ram\u00edrez","year":"2021","journal-title":"Atmos. Chem. Phys. Discuss."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2020.118176","article-title":"Assessment of biomass-burning types and transport over Thailand and the associated health risks","volume":"247","author":"Punsompong","year":"2021","journal-title":"Atmos. Environ."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0305","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1038\/s41598-020-79361-4","article-title":"Changing patterns in aerosol vertical distribution over South and East Asia","volume":"11","author":"Ratnam","year":"2021","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.scitotenv.2025.180535_bb0310","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1175\/JAS3385.1","article-title":"The MODIS aerosol algorithm, products, and validation","volume":"62","author":"Remer","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2024.114115","article-title":"Time series retrieval of multi-wavelength aerosol optical depth by adapting transformer (TMAT) using Himawari-8 AHI data","volume":"305","author":"She","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0320","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2021.151490","article-title":"Long-term variations in aerosol optical properties, types, and radiative forcing in the Sichuan Basin, Southwest China","volume":"807","author":"Shu","year":"2022","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0325","doi-asserted-by":"crossref","first-page":"14035","DOI":"10.1109\/JSTARS.2024.3408251","article-title":"An adaptive dark-target algorithm for retrieving land AOD applied to FY-4B\/AGRI data","volume":"17","author":"Si","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"issue":"6","key":"10.1016\/j.scitotenv.2025.180535_bb0330","doi-asserted-by":"crossref","first-page":"3375","DOI":"10.5194\/amt-13-3375-2020","article-title":"The AERONET version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to version 2","volume":"13","author":"Sinyuk","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0335","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.atmosenv.2018.08.012","article-title":"Diurnal and seasonal variability of PM2. 5 and AOD in North China plain: comparison of MERRA-2 products and ground measurements","volume":"191","author":"Song","year":"2018","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2025.121365","article-title":"Enhanced continuous aerosol optical depth (AOD) estimation using geostationary satellite data: focusing on nighttime AOD over East Asia","volume":"358","author":"Song","year":"2025","journal-title":"Atmos. Environ."},{"issue":"4","key":"10.1016\/j.scitotenv.2025.180535_bb0345","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1136\/jech.53.4.235","article-title":"Studying seasonality by using sine and cosine functions in regression analysis","volume":"53","author":"Stolwijk","year":"1999","journal-title":"J. Epidemiol. Community Health"},{"key":"10.1016\/j.scitotenv.2025.180535_bb0350","series-title":"An Introduction to Boundary Layer Meteorology","volume":"Vol. 13","author":"Stull","year":"2012"},{"issue":"3","key":"10.1016\/j.scitotenv.2025.180535_bb0355","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e14261","article-title":"Airborne particulate matter from biomass burning in Thailand: recent issues, challenges, and options","volume":"9","author":"Suriyawong","year":"2023","journal-title":"Heliyon"},{"key":"10.1016\/j.scitotenv.2025.180535_bb0360","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.atmosenv.2016.07.021","article-title":"Effect of hygroscopic growth on the aerosol light-scattering coefficient: a review of measurements, techniques and error sources","volume":"141","author":"Titos","year":"2016","journal-title":"Atmos. Environ."},{"issue":"D24","key":"10.1016\/j.scitotenv.2025.180535_bb0365","doi-asserted-by":"crossref","DOI":"10.1029\/2007JD008809","article-title":"Aerosols and surface UV products from ozone monitoring instrument observations: an overview","volume":"112","author":"Torres","year":"2007","journal-title":"J. Geophys. Res. Atmos."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0370","first-page":"1","article-title":"TROPOMI aerosol products: evaluation and observations of synoptic scale carbonaceous aerosol plumes during 2018\u20132020","volume":"2020","author":"Torres","year":"2020","journal-title":"Atmos. Meas. Tech. Discuss."},{"issue":"12","key":"10.1016\/j.scitotenv.2025.180535_bb0375","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.3390\/atmos13121983","article-title":"Spatial-temporal variation of AOD based on MAIAC AOD in East Asia from 2011 to 2020","volume":"13","author":"Wang","year":"2022","journal-title":"Atmosphere"},{"key":"10.1016\/j.scitotenv.2025.180535_bb0380","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.isprsjprs.2024.06.022","article-title":"Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS","volume":"214","author":"Wang","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0385","first-page":"4103517","article-title":"An improved aerosol retrieval algorithm for FY-4A\/AGRI data based on the GRASP framework","volume":"63","author":"Wang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0390","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2020.117695","article-title":"Aerosol data assimilation using data from Fengyun-4A, a next-generation geostationary meteorological satellite","volume":"237","author":"Xia","year":"2020","journal-title":"Atmos. Environ."},{"issue":"33","key":"10.1016\/j.scitotenv.2025.180535_bb0395","doi-asserted-by":"crossref","first-page":"5970","DOI":"10.1016\/j.atmosenv.2011.08.002","article-title":"Validation of MODIS aerosol optical depth product over China using CARSNET measurements","volume":"45","author":"Xie","year":"2011","journal-title":"Atmos. Environ."},{"issue":"9","key":"10.1016\/j.scitotenv.2025.180535_bb0400","doi-asserted-by":"crossref","first-page":"5199","DOI":"10.5194\/acp-24-5199-2024","article-title":"Dust storms from the Taklamakan Desert significantly darken snow surface on surrounding mountains","volume":"24","author":"Xing","year":"2024","journal-title":"Atmos. Chem. Phys."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0405","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.144241","article-title":"Spatiotemporal relationship between Himawari-8 hourly columnar aerosol optical depth (AOD) and ground-level PM2. 5 mass concentration in mainland China","volume":"765","author":"Xu","year":"2021","journal-title":"Sci. Total Environ."},{"issue":"5_3","key":"10.1016\/j.scitotenv.2025.180535_bb0410","first-page":"933","article-title":"Retrieval of hourly aerosol optical depth using top-of-atmosphere reflectance from GOCI-II and machine learning over South Korea","volume":"39","author":"Yang","year":"2023","journal-title":"Korean J. Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0415","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3107542","article-title":"Estimation of the hourly aerosol optical depth from GOCI geostationary satellite data: deep neural network, machine learning, and physical models","volume":"60","author":"Yeom","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0420","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1080\/15481603.2022.2060596","article-title":"Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm","volume":"59","author":"Zhang","year":"2022","journal-title":"GISci. Remote Sens."},{"key":"10.1016\/j.scitotenv.2025.180535_bb0425","first-page":"1","article-title":"Himawari-8 high temporal resolution AOD products recovery: nested bayesian maximum entropy fusion blending GEO with SSO satellite observations","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb2000","doi-asserted-by":"crossref","first-page":"2388921","DOI":"10.1080\/15481603.2024.2388921","article-title":"Improving assessment of population exposure and health impacts to PM2. 5 with high spatial and temporal data","volume":"61","author":"Zhao","year":"2024","journal-title":"GISci. Remote Sens."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.180535_bb0430","doi-asserted-by":"crossref","DOI":"10.1080\/15481603.2024.2363576","article-title":"Simulating urban expansion with interpretable cycle recurrent neural networks","volume":"61","author":"Zhu","year":"2024","journal-title":"GISci. Remote Sens."}],"container-title":["Science of The Total Environment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969725021758?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969725021758?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T13:58:53Z","timestamp":1766066333000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0048969725021758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":88,"alternative-id":["S0048969725021758"],"URL":"https:\/\/doi.org\/10.1016\/j.scitotenv.2025.180535","relation":{},"ISSN":["0048-9697"],"issn-type":[{"value":"0048-9697","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning","name":"articletitle","label":"Article Title"},{"value":"Science of The Total Environment","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.scitotenv.2025.180535","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"180535"}}