{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T02:18:42Z","timestamp":1768443522622,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T00:00:00Z","timestamp":1526947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["UG3 OD023337"],"award-info":[{"award-number":["UG3 OD023337"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P30 ES023515"],"award-info":[{"award-number":["P30 ES023515"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R00 ES023450"],"award-info":[{"award-number":["R00 ES023450"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 \u00d7 1 km AOD product for Aqua and Terra satellites across the Northeastern\/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30\u2013210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.<\/jats:p>","DOI":"10.3390\/rs10050803","type":"journal-article","created":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T03:14:24Z","timestamp":1527045264000},"page":"803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4312-5957","authenticated-orcid":false,"given":"Allan C.","family":"Just","sequence":"first","affiliation":[{"name":"Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margherita M.","family":"De Carli","sequence":"additional","affiliation":[{"name":"Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Shtein","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6450-8047","authenticated-orcid":false,"given":"Michael","family":"Dorman","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1105-5739","authenticated-orcid":false,"given":"Alexei","family":"Lyapustin","sequence":"additional","affiliation":[{"name":"National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itai","family":"Kloog","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1097\/MOP.0000000000000326","article-title":"Satellite remote sensing in epidemiological studies","volume":"28","author":"Just","year":"2016","journal-title":"Curr. Opin. Pediatr."},{"key":"ref_2","first-page":"9","article-title":"Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables","volume":"116","author":"Lyapustin","year":"2011","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lyapustin, A., Wang, Y., Laszlo, I., Kahn, R., Korkin, S., Remer, L., Levy, R., and Reid, J.S. (2011). Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. J. Geophys. Res. Atmos., 116.","DOI":"10.1029\/2010JD014986"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.atmosenv.2014.07.014","article-title":"A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data","volume":"95","author":"Kloog","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3762","DOI":"10.1021\/acs.est.5b05833","article-title":"Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors","volume":"50","author":"Martin","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.atmosenv.2016.02.002","article-title":"A hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regression","volume":"131","author":"Di","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10907","DOI":"10.5194\/acp-13-10907-2013","article-title":"A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions","volume":"13","author":"Chudnovsky","year":"2013","journal-title":"Atmos. Chem. Phys."},{"key":"ref_8","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":"ref_9","first-page":"345","article-title":"Completion of the 2011 national land cover database for the conterminous united states\u2014Representing a decade of land cover change information","volume":"81","author":"Homer","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","first-page":"18","article-title":"Classification and regression by randomforest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_11","unstructured":"Ridgeway, G. (2018, May 21). Generalized Boosted Regression Models. Available online: https:\/\/www.google.com.hk\/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwihxYfLjpjbAhXMx7wKHao5AHMQFgglMAA&url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fgbm%2Fgbm.pdf&usg=AOvVaw0ALtYnS1e_kYe-cOK9ImJD."},{"key":"ref_12","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., and Tang, Y. (2017, January 01). Xgboost: Extreme Gradient Boosting. Available online: cran.fhcrc.org\/web\/packages\/xgboost\/vignettes\/xgboost.pdf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_14","unstructured":"Ishwaran, H., and Kogalur, U.B. (2017, December 21). Random Forests for Survival, Regression, and Classification (Rf-Src). Available online: https:\/\/www.google.com.hk\/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwilocz_lZjbAhXJU7wKHfp6AQwQFgglMAA&url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2FrandomForestSRC%2FrandomForestSRC.pdf&usg=AOvVaw38a2v6X_POBwVKEC99-EFa."},{"key":"ref_15","first-page":"1625","article-title":"Confidence intervals for random forests: The jackknife and the infinitesimal jackknife","volume":"15","author":"Wager","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","unstructured":"Brokamp, C. (2017, December 21). Rfinfer: Inference for Random Forests. Available online: https:\/\/github.com\/cole-brokamp\/RFinfer."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8576","DOI":"10.1021\/acs.est.5b00859","article-title":"Using high-resolution satellite aerosol optical depth to estimate daily PM2.5 geographical distribution in mexico city","volume":"49","author":"Just","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.atmosenv.2015.10.004","article-title":"Estimating daily PM2.5 and PM10 across the complex geo-climate region of israel using maiac satellite-based aod data","volume":"122","author":"Kloog","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1177\/0049124115585360","article-title":"A unified approach to measurement error and missing data","volume":"46","author":"Blackwell","year":"2015","journal-title":"Sociol. Methods Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Marshall, A., Altman, D.G., Holder, R.L., and Royston, P. (2009). Combining estimates of interest in prognostic modelling studies after multiple imputation: Current practice and guidelines. BMC Med. Res. Methodol., 9.","DOI":"10.1186\/1471-2288-9-57"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5886","DOI":"10.1002\/2016GL069298","article-title":"Aerosol data assimilation using data from himawari-8, a next-generation geostationary meteorological satellite","volume":"43","author":"Yumimoto","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1175\/BAMS-D-14-00007.1","article-title":"Real-time simulation of the goes-r abi for user readiness and product evaluation","volume":"97","author":"Greenwald","year":"2016","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.1021\/es505846r","article-title":"Spatiotemporal prediction of fine particulate matter during the 2008 northern california wildfires using machine learning","volume":"49","author":"Reid","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_25","unstructured":"Chen, T., and He, T. (2015, January 8\u201313). Higgs boson discovery with boosted trees. Proceedings of the NIPS 2014 Workshop on High-Energy Physics and Machine Learning, Montreal, QC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Babajide Mustapha, I., and Saeed, F. (2016). Bioactive molecule prediction using extreme gradient boosting. Molecules, 21.","DOI":"10.3390\/molecules21080983"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4353","DOI":"10.5194\/amt-7-4353-2014","article-title":"Scientific impact of modis C5 calibration degradation and C6+ improvements","volume":"7","author":"Lyapustin","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_29","unstructured":"Holben, B., Eck, T., Schafer, J., Giles, D., and Sorokin, M. (2017, August 01). Distributed Regional Aerosol Gridded Observation Networks (Dragon) White Paper, Available online: http:\/\/aeronet.gsfc.nasa.gov\/new_web\/Documents\/DRAGON_White_Paper_A_system_of_experiment.pdf."},{"key":"ref_30","unstructured":"NASA Earth Observatory (2017, September 16). Smoke over the Mid-Atlantic, Available online: https:\/\/earthobservatory.nasa.gov\/NaturalHazards\/view.php?id=86024."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.atmosenv.2014.05.061","article-title":"Satellite data of atmospheric pollution for u.S. Air quality applications: Examples of applications, summary of data end-user resources, answers to faqs, and common mistakes to avoid","volume":"94","author":"Duncan","year":"2014","journal-title":"Atmos. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/803\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:05:22Z","timestamp":1760195122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/803"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,22]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050803"],"URL":"https:\/\/doi.org\/10.3390\/rs10050803","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,22]]}}}