{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:50:37Z","timestamp":1769151037309,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010700","name":"National Institute of Environmental Research","doi-asserted-by":"publisher","award":["NIER-2019-01-01-027"],"award-info":[{"award-number":["NIER-2019-01-01-027"]}],"id":[{"id":"10.13039\/501100010700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003719","name":"Korea Aerospace Research Institute","doi-asserted-by":"publisher","award":["FR21J00"],"award-info":[{"award-number":["FR21J00"]}],"id":[{"id":"10.13039\/501100003719","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 \u03bcm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 \u03bcg\/m3, mean bias error (MBE) = \u22120.340 \u03bcg\/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 \u03bcg\/m3, MBE = 0.293 \u03bcg\/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (&lt;10 \u03bcg\/m3 and 1 \u03bcg\/m3, respectively) for the hold-out validation and cross-validation.<\/jats:p>","DOI":"10.3390\/rs13112121","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T03:45:29Z","timestamp":1622432729000},"page":"2121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-5779","authenticated-orcid":false,"given":"Changsuk","family":"Lee","sequence":"first","affiliation":[{"name":"Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyunghwa","family":"Lee","sequence":"additional","affiliation":[{"name":"Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sangmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhyeok","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seungtaek","family":"Jeong","sequence":"additional","affiliation":[{"name":"Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 305-806, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2321-731X","authenticated-orcid":false,"given":"Jongmin","family":"Yeom","sequence":"additional","affiliation":[{"name":"Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 305-806, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13133","DOI":"10.5194\/acp-15-13133-2015","article-title":"Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument","volume":"15","author":"Xu","year":"2015","journal-title":"Atmos. 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