{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T17:56:33Z","timestamp":1779904593532,"version":"3.53.1"},"reference-count":16,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,8]],"date-time":"2020-03-08T00:00:00Z","timestamp":1583625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Landsat 8 Operational Land Imager (OLI) has a panchromatic band (503\u2013676 nm) that can be used to derive a novel virtual orange band (590\u2013635 nm) by using the multispectral green band and red band components. The orange band is useful for the accurate detection and quantification of phycocyanin (PC), an accessory pigment in toxin-producing cyanobacterial blooms, because of the specific light absorption characteristics of PC around 600\u2013625 nm. In this study, we compared the Landsat 8 OLI\u2019s and Sentinel-3 Ocean and Land Color Instrument\u2019s (OLCI) derived orange band reflectance and PC products corresponding to a same-date overpass during a severe cyanobacterial bloom in Lake Erie, USA. The goal was to determine if the OLI\u2019s virtual orange band can produce results equivalent to the OLCI\u2019s actual orange band. Band-by-band match-ups used the OLI\u2019s top-of-atmosphere (TOA) reflectance versus TOA reflectance from the OLCI, and surface reflectance (SR) from the OLI versus SR from the OLCI. A significant correlation was observed between the OLI\u2019s and OLCI\u2019s derived orange band TOA reflectance (R2 = 0.86; p &lt; 0.001; NRMSE = 9.01%) and orange band SR (R2 = 0.93; p &lt; 0.001; NRMSE = 20.23%). The PC map produced using the best-fit empirical models from both sensors showed similar PC spatial patterns and concentration levels in the western basin of Lake Erie. The results from this research are particularly important for the study of smaller inland waterbodies with the 30 m resolution of the OLI, which cannot be studied with the 300 m resolution of OLCI data, and for analyzing historical bloom events before the launch of the OLCI. Although more analysis and validation need to be conducted, this study opens up Landsat 8\u2019s applicability in research on cyanobacterial harmful algal blooms (cyanoHABs).<\/jats:p>","DOI":"10.3390\/rs12050868","type":"journal-article","created":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T05:37:34Z","timestamp":1583732254000},"page":"868","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Landsat 8 Virtual Orange Band for Mapping Cyanobacterial Blooms"],"prefix":"10.3390","volume":"12","author":[{"given":"Abhishek","family":"Kumar","sequence":"first","affiliation":[{"name":"Center for Geospatial Research, Department of Geography, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8192-7681","authenticated-orcid":false,"given":"Deepak R.","family":"Mishra","sequence":"additional","affiliation":[{"name":"Center for Geospatial Research, Department of Geography, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nirav","family":"Ilango","sequence":"additional","affiliation":[{"name":"Center for Geospatial Research, Department of Geography, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2014.06.009","article-title":"Remote sensing of freshwater cyanobacteria: An extended IOP inversion model of inland waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin","volume":"157","author":"Li","year":"2015","journal-title":"Remote Sens. 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Remote Sens., 8.","DOI":"10.3390\/rs8030212"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/868\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:05:12Z","timestamp":1760173512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,8]]},"references-count":16,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050868"],"URL":"https:\/\/doi.org\/10.3390\/rs12050868","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,8]]}}}