{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T16:23:09Z","timestamp":1769876589342,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T00:00:00Z","timestamp":1547078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX13AJ24A"],"award-info":[{"award-number":["NNX13AJ24A"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX15AK94G"],"award-info":[{"award-number":["NNX15AK94G"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m \u00d7 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (&gt;1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes &gt; 0.96; coefficient of determination r2 &gt; 0.90; root mean square deviation &lt; 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.<\/jats:p>","DOI":"10.3390\/rs11020122","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:10:16Z","timestamp":1547179816000},"page":"122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhongbin","family":"Li","sequence":"first","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-0250","authenticated-orcid":false,"given":"David P.","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment, &amp; Spatial Sciences and Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4470-3616","authenticated-orcid":false,"given":"Hankui K.","family":"Zhang","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]},{"given":"Eric F.","family":"Vermote","sequence":"additional","affiliation":[{"name":"Terrestrial Information System Laboratory (Code 619), NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7767-890X","authenticated-orcid":false,"given":"Haiyan","family":"Huang","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17373","DOI":"10.1073\/pnas.1419604111","article-title":"Elucidating severe urban haze formation in China","volume":"111","author":"Guo","year":"2014","journal-title":"Proc. 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