{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T23:15:23Z","timestamp":1775258123054,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,12]],"date-time":"2021-06-12T00:00:00Z","timestamp":1623456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000139","name":"U.S. Environmental Protection Agency","doi-asserted-by":"publisher","award":["Great Lakes Restoration Initiative"],"award-info":[{"award-number":["Great Lakes Restoration Initiative"]}],"id":[{"id":"10.13039\/100000139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite imagery has been used to monitor and assess Harmful Algal Blooms (HABs), specifically, cyanobacterial blooms in Lake Erie (the USA and Canada) for over twelve years. In recent years, imagery has been applied to the other Great Lakes as well as other U.S. lakes. The key algorithm used in this monitoring system is the cyanobacterial index (CI), a measure of the chlorophyll found in cyanobacterial blooms. The CI is a \u201cspectral shape\u201d (or curvature) algorithm, which is a form of the second derivative around the 681 nm (MERIS\/OLCI) or 678 nm (MODIS) band, which is robust and implicitly includes an atmospheric correction, allowing reliable use for many more scenes than analytical algorithms. Monitoring of cyanobacterial blooms with the CI began with the European Space Agency\u2019s (ESA) Medium Resolution Imaging Spectrometer (MERIS) sensor (2002\u20132012). With the loss of data from MERIS in the spring of 2012, the monitoring system shifted to using NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS has bands that allow computation of a CI product, which was intercalibrated with MERIS at the time to establish a conversion of MODIS CI to MERIS CI. In 2016, ESA launched the Ocean and Land Color Imager (OLCI), the replacement for MERIS, on the Sentinel-3 spacecraft. MODIS can serve two purposes. It can provide a critical data set for the blooms of 2012\u20132015, and it offers a bridge from MERIS to OLCI. We propose a basin-wide integrated technique for intercalibrating the CI algorithm from MODIS to both MERIS and OLCI. This method allowed us to intercalibrate OLCI CI to MERIS CI, which would then allow the production of a 20-year and ongoing record of cyanobacterial bloom activity. This approach also allows updates as sensor calibrations change or new sensors are launched, and it could be readily applied to spectral shape algorithms.<\/jats:p>","DOI":"10.3390\/rs13122305","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T22:25:46Z","timestamp":1623709546000},"page":"2305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Intercalibration of MERIS, MODIS, and OLCI Satellite Imagers for Construction of Past, Present, and Future Cyanobacterial Biomass Time Series"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7282-0866","authenticated-orcid":false,"given":"Timothy T.","family":"Wynne","sequence":"first","affiliation":[{"name":"National Centers for Coastal Ocean Science, National Ocean Service, National Oceanic and Atmospheric Administration, 1305 East-West Highway, Silver Spring, MD 20910, USA"}]},{"given":"Sachidananda","family":"Mishra","sequence":"additional","affiliation":[{"name":"National Centers for Coastal Ocean Science, National Ocean Service, National Oceanic and Atmospheric Administration, 1305 East-West Highway, Silver Spring, MD 20910, USA"},{"name":"CSS, Inc., Fairfax, VA 22030, USA"}]},{"given":"Andrew","family":"Meredith","sequence":"additional","affiliation":[{"name":"National Centers for Coastal Ocean Science, National Ocean Service, National Oceanic and Atmospheric Administration, 1305 East-West Highway, Silver Spring, MD 20910, USA"},{"name":"CSS, Inc., Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6293-8000","authenticated-orcid":false,"given":"R. Wayne","family":"Litaker","sequence":"additional","affiliation":[{"name":"National Centers for Coastal Ocean Science, National Ocean Service, National Oceanic and Atmospheric Administration, 1305 East-West Highway, Silver Spring, MD 20910, USA"},{"name":"CSS, Inc., Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5531-6860","authenticated-orcid":false,"given":"Richard P.","family":"Stumpf","sequence":"additional","affiliation":[{"name":"National Centers for Coastal Ocean Science, National Ocean Service, National Oceanic and Atmospheric Administration, 1305 East-West Highway, Silver Spring, MD 20910, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3389\/fmars.2019.00123","article-title":"Barriers and Bridges in Abating Coastal Eutrophication","volume":"6","author":"Boesch","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6448","DOI":"10.1073\/pnas.1216006110","article-title":"Record-setting algal bloom in Lake Erie caused by agricultural meteorological trends consistent with expected future conditions","volume":"110","author":"Michalak","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1126\/science.1155398","article-title":"Blooms like it hot","volume":"320","author":"Paerl","year":"2008","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chorus, I., and Bartram, J. (1999). Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring and Management, World Health Organization.","DOI":"10.4324\/9780203478073"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ecss.2005.11.024","article-title":"Monitoring cyanobacterial blooms by satellite remote sensing","volume":"67","author":"Kutser","year":"2006","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4401","DOI":"10.1080\/01431160802562305","article-title":"Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters","volume":"30","author":"Kutser","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/TGRS.2012.2228654","article-title":"Overview of Intercalibration of Satellite Instruments","volume":"51","author":"Chander","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8639","DOI":"10.1002\/2013JD020702","article-title":"Intercalibration of GOES Imager visible channels over the Sonoran Desert","volume":"119","author":"Yu","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bouvet, M., Thome, K., Berthelot, B., Bialek, A., Czapla-Myers, J., Fox, N.P., Goryl, P., Henry, P., Ma, L., and Marcq, S. (2019). RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range. Remote Sens., 11.","DOI":"10.3390\/rs11202401"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6668","DOI":"10.1080\/01431161.2013.804228","article-title":"Comparing MODIS and MERIS spectral shapes for cyanobacterial bloom detection","volume":"34","author":"Wynne","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.marpolbul.2014.06.053","article-title":"Comparison of the efficacy of MODIS and MERIS data for detecting cyanobacterial blooms in the southern Caspian Sea","volume":"87","author":"Moradi","year":"2014","journal-title":"Mar. Pollut. Bull."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jglr.2012.10.003","article-title":"Evolution of a cyanobacterial bloom forecast system in western Lake Erie: Development and initial evaluation","volume":"39","author":"Wynne","year":"2013","journal-title":"J. Great Lakes Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.rse.2014.06.008","article-title":"Evaluation of cyanobacteria cell count de-tection derived from MERIS imagery across the eastern USA","volume":"157","author":"Lunetta","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schaeffer, B., Loftin, K., Stumpf, R., and Werdell, P. (2015). Agencies Collaborate, Develop a Cyanobacteria Assessment Network. EOS, 96.","DOI":"10.1029\/2015EO038809"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.ecolind.2017.04.046","article-title":"Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking water sources","volume":"80","author":"Clark","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18310","DOI":"10.1038\/s41598-019-54453-y","article-title":"Measurement of Cyanobacterial Bloom Magnitude using Satellite Remote Sensing","volume":"9","author":"Mishra","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1080\/01431160802007640","article-title":"Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes","volume":"29","author":"Wynne","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.4319\/lo.2010.55.5.2025","article-title":"Characterizing a cyanobacterial bloom in western Lake Erie using satellite imagery and metrological data","volume":"55","author":"Wynne","year":"2010","journal-title":"Limnol. Oceanogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101999","DOI":"10.1016\/j.hal.2021.101999","article-title":"Cyanobacterial bloom phenology in Saginaw Bay from MODIS and a comparative look with western Lake Erie","volume":"103","author":"Wynne","year":"2021","journal-title":"Harmful Algae"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4147","DOI":"10.1080\/01431161.2016.1207265","article-title":"Cyanobacterial blooms in three eutrophic basins of the Great Lakes: A comparative analysis using remote sensing","volume":"37","author":"Sayers","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6745","DOI":"10.1021\/acs.est.7b00856","article-title":"Ecophysiological examination of the Lake Erie Mi-crocystis bloom in 2014: Linkages between biology and the water supply shutdown of Toledo, Ohio","volume":"51","author":"Steffen","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_22","unstructured":"NOAA (2021, January 11). Harmful Algal Bloom Monitoring System, Available online: https:\/\/coastalscience.noaa.gov\/research\/stressor-impacts-mitigation\/hab-monitoring-system\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.jglr.2012.10.001","article-title":"Great Lakes total phosphorus revisited: 1. Loading analysis and update (1994\u20132008)","volume":"38","author":"Dolan","year":"2012","journal-title":"J. Great Lakes Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1021\/es00119a007","article-title":"Retrospective analysis of the response of Saginaw Bay, Lake Huron, to reductions in phosphorus loadings","volume":"18","author":"Bierman","year":"1984","journal-title":"Environ. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.jglr.2014.05.001","article-title":"Phosphorus loading to Lake Erie from the Maumee, Sandusky and Cuyahoga rivers: The importance of bioavailability","volume":"40","author":"Baker","year":"2014","journal-title":"J. Great Lakes Res."},{"key":"ref_26","unstructured":"Wynne, T.T., Meredith, A., Stumpf, R.P., Briggs, T.O., and Litaker, R.W. (2018). Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines, NOAA. NOAA Technical Memorandum NOS NCCOS 252."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.3390\/toxins7051649","article-title":"Spatial and Temporal Patterns in the Seasonal Distribution of Toxic Cyanobacteria in Western Lake Erie from 2002\u20132014","volume":"7","author":"Wynne","year":"2015","journal-title":"Toxins"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2014.10.010","article-title":"Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters","volume":"156","author":"Matthews","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2852","DOI":"10.1364\/AO.18.002852","article-title":"Adjacency effects on imaging by surface reflection and atmospheric scattering: Cross radiance to zenith","volume":"18","author":"Otterman","year":"1979","journal-title":"Appl. Opt."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5934","DOI":"10.1080\/01431161.2017.1331476","article-title":"Intercalibration between DMSP\/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria\u2019s major human settlement during Syrian Civil War","volume":"38","author":"Li","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2011). Leave-One-Out Cross-Validation. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7404","DOI":"10.1364\/OE.26.007404","article-title":"Performance metrics for the assessment of satellite data products: An ocean color case study","volume":"26","author":"Seegers","year":"2018","journal-title":"Opt. Express"},{"key":"ref_33","first-page":"1114910","article-title":"Comparison of two data sets from two different satellite sensors at @490 nm in the same space-temporal window over the Gulf of California area","volume":"Volume 11149","year":"2019","journal-title":"Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1016\/j.jglr.2016.08.007","article-title":"What motivates farmers to apply phosphorus at the \u201cright\u201d time? Survey evidence from the Western Basin of Lake Erie","volume":"42","author":"Zhang","year":"2016","journal-title":"J. Great Lakes Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1364\/OE.18.000401","article-title":"Adjustment of ocean color sensor calibration through multi-band statistics","volume":"18","author":"Stumpf","year":"2010","journal-title":"Opt. Express"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6796","DOI":"10.1364\/AO.47.006796","article-title":"Cross calibration of ocean-color bands from moderate resolution imaging spectroradiometer on Terra platform","volume":"47","author":"Kwiatkowska","year":"2008","journal-title":"Appl. Opt."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TGRS.2011.2160552","article-title":"Corrections to the Calibration of MODIS Aqua Ocean Color Bands Derived From SeaWiFS Data","volume":"50","author":"Meister","year":"2011","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6209","DOI":"10.1080\/01431160802178110","article-title":"Global monitoring of plankton blooms using MERIS MCI","volume":"29","author":"Gower","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gholizadeh, M.H., Melesse, A.M., and Reddi, L. (2016). A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques. Sensors, 16.","DOI":"10.3390\/s16081298"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10285","DOI":"10.1109\/TGRS.2019.2933251","article-title":"Novel spectra-derived features for empirical retrieval of water quality parameters: Demonstrations for OLI, MSI, and OLCI sensors","volume":"57","author":"Bovolo","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2305\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:13:38Z","timestamp":1760163218000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,12]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122305"],"URL":"https:\/\/doi.org\/10.3390\/rs13122305","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,12]]}}}