{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T22:26:06Z","timestamp":1772231166721,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Army Corps of Engineers\u2019 Aquatic Nuisance Species Research Program","award":["W81EWF21876042"],"award-info":[{"award-number":["W81EWF21876042"]}]},{"name":"Great Lakes Restoration Initiative (GLRI)","award":["W81EWF21876042"],"award-info":[{"award-number":["W81EWF21876042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-based monitoring of cyanobacterial harmful algal blooms (CyanoHABs) heavily utilizes historical Envisat-MERIS and current Sentinel-OLCI observations due to the availability of the 620 nm and 709 nm bands. The permanent loss of communication with Envisat in April 2012 created an observational gap from 2012 until the operationalization of OLCI in 2016. Although MODIS-Terra has been used to bridge the gap from 2012 to 2015, differences in band architecture and the absence of the 709 nm band have complicated generating a consistent and continuous CyanoHAB monitoring product. Moreover, several Terra bands often saturate during extreme high-concentration CyanoHAB events. This study trained a fully connected deep network (CyanNet) to model MERIS-Cyanobacteria Index (CI)\u2014a key satellite algorithm for detecting and quantifying cyanobacteria. The network was trained with Rayleigh-corrected surface reflectance at 12 Terra bands from 2002\u20132008, 2010\u20132012, and 2017\u20132021 and validated with data from 2009 and 2016 in Lake Okeechobee. Model performance was satisfactory, with a ~17% median difference in Lake Okeechobee annual bloom magnitude. The median difference was ~36% with 10-day Chlorophyll-a time series data, with differences often due to variations in data availability, clouds or glint. Without further regional training, the same network performed well in Lake Apopka, Lake George, and western Lake Erie. Validation success, especially in Lake Erie, shows the generalizability of CyanNet and transferability to other geographic regions.<\/jats:p>","DOI":"10.3390\/rs15225291","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T03:42:40Z","timestamp":1699501360000},"page":"5291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6613-3103","authenticated-orcid":false,"given":"Sachidananda","family":"Mishra","sequence":"first","affiliation":[{"name":"Consolidated Safety Services Inc., Fairfax, VA 22030, USA"},{"name":"National Centers for Coastal Ocean Science, National Oceanic and Atmospheric Administration, Silver Spring, MD 20910, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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 Oceanic and Atmospheric Administration, Silver Spring, MD 20910, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9651-7132","authenticated-orcid":false,"given":"Andrew","family":"Meredith","sequence":"additional","affiliation":[{"name":"Consolidated Safety Services Inc., Fairfax, VA 22030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Stumpf, R.P., Wynne, T.T., Baker, D.B., and Fahnenstiel, G.L. (2012). Interannual variability of cyanobacterial blooms in Lake Erie. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0042444"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108990","DOI":"10.1016\/j.ecolind.2022.108990","article-title":"Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales","volume":"140","author":"Schaeffer","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kutser, T., Soomets, T., Toming, K., Uiboupin, R., Arikas, A., Vahter, K., and Paavel, B. (2018, January 12\u201315). Assessing the Baltic Sea Water Quality with Sentinel-3 OLCI Imagery. Proceedings of the 2018 IEEE\/OES Baltic International Symposium (BALTIC), Klaipeda, Lithuania.","DOI":"10.1109\/BALTIC.2018.8634849"},{"key":"ref_4","first-page":"62","article-title":"Near-term forecasting of cyanobacteria and harmful algal blooms in lakes using simple univariate methods with satellite remote sensing data","volume":"13","year":"2022","journal-title":"Inland Waters"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106999","DOI":"10.1016\/j.ecolind.2020.106999","article-title":"EOLakeWatch; delivering a comprehensive suite of remote sensing algal bloom indices for enhanced monitoring of Canadian eutrophic lakes","volume":"121","author":"Binding","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105976","DOI":"10.1016\/j.ecolind.2019.105976","article-title":"Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing","volume":"111","author":"Coffer","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165253","DOI":"10.1016\/j.scitotenv.2023.165253","article-title":"Recent changes in cyanobacteria algal bloom magnitude in large lakes across the contiguous United States","volume":"897","author":"Mishra","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.rse.2014.06.008","article-title":"Evaluation of cyanobacteria cell count detection derived from MERIS imagery across the eastern USA","volume":"157","author":"Lunetta","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.rse.2013.02.004","article-title":"Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach","volume":"133","author":"Mishra","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.hal.2016.01.005","article-title":"Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria","volume":"54","author":"Stumpf","year":"2016","journal-title":"Harmful Algae"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.1080\/01431160500075857","article-title":"Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer","volume":"26","author":"Gower","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.rse.2012.05.032","article-title":"An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters","volume":"124","author":"Matthews","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.rse.2011.10.016","article-title":"Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters","volume":"117","author":"Mishra","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"111227","DOI":"10.1016\/j.rse.2019.111227","article-title":"Remote detection of cyanobacteria blooms in an optically shallow subtropical lagoonal estuary using MODIS data","volume":"231","author":"Cannizzaro","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"124","DOI":"10.3389\/fmars.2017.00124","article-title":"Satellite remote sensing of drinking water intakes in Lake Erie for cyanobacteria population using two MODIS-based indicators as a potential tool for toxin tracking","volume":"4","author":"Zhang","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Konik, M., Bradtke, K., Sto\u0144-Egiert, J., Soja-Wo\u017aniak, M., \u015aliwi\u0144ska-Wilczewska, S., and Darecki, M. (2023). Cyanobacteria Index as a Tool for the Satellite Detection of Cyanobacteria Blooms in the Baltic Sea. Remote Sens., 15.","DOI":"10.3390\/rs15061601"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"237","DOI":"10.4319\/lo.2005.50.1.0237","article-title":"Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water","volume":"50","author":"Simis","year":"2005","journal-title":"Limnol. Oceanogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"C01011","DOI":"10.1029\/2011JC007395","article-title":"Chlorophyll aalgorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference","volume":"117","author":"Hu","year":"2012","journal-title":"J. Geophys. Res. Ocean"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1109\/TGRS.2017.2763456","article-title":"Correction of sensor saturation effects in MODIS oceanic particulate inorganic carbon","volume":"56","author":"Land","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wynne, T.T., Mishra, S., Meredith, A., Litaker, R.W., and Stumpf, R.P. (2021). Intercalibration of MERIS, MODIS, and OLCI Satellite Imagers for Construction of Past, Present, and Future Cyanobacterial Biomass Time Series. Remote Sens., 13.","DOI":"10.3390\/rs13122305"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zeng, C., and Binding, C.E. (2021). Consistent multi-mission measures of inland water algal bloom spatial extent using MERIS, MODIS and OLCI. Remote Sens., 13.","DOI":"10.3390\/rs13173349"},{"key":"ref_25","first-page":"48","article-title":"Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines","volume":"252","author":"Wynne","year":"2018","journal-title":"NOAA Tech. Memo. NOS NCCOS"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104826","DOI":"10.1016\/j.dib.2019.104826","article-title":"Envisat MERIS and Sentinel-3 OLCI satellite lake biophysical water quality flag dataset for the contiguous United States","volume":"28","author":"Urquhart","year":"2020","journal-title":"Data Brief"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"102191","DOI":"10.1016\/j.hal.2022.102191","article-title":"A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across US lakes","volume":"115","author":"Whitman","year":"2022","journal-title":"Harmful Algae"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1080\/01431160701355272","article-title":"Terra and Aqua MODIS inter-comparison of three reflective solar bands using AVHRR onboard the NOAA-KLM satellites","volume":"29","author":"Wu","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"1063","article-title":"Cloud implementation of logistic regression for hyperspectral image classification","volume":"1","author":"Haut","year":"2020","journal-title":"IEEE J. Miniaturization Air Space Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1016\/j.jglr.2016.08.006","article-title":"Forecasting annual cyanobacterial bloom biomass to inform management decisions in Lake Erie","volume":"42","author":"Stumpf","year":"2016","journal-title":"J. Great Lakes Res."},{"key":"ref_32","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_33","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 meteorological data","volume":"55","author":"Wynne","year":"2010","journal-title":"Limnol. Oceanogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112685","DOI":"10.1016\/j.rse.2021.112685","article-title":"Satellites for long-term monitoring of inland US lakes: The MERIS time series and application for chlorophyll-a","volume":"266","author":"Seegers","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_35","unstructured":"Egan, J.P. (1975). Signal Detection Theory and ROC Analysis, Academic Press."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","first-page":"12","article-title":"Imbalanced learning: Foundations, algorithms, and applications","volume":"1","author":"Haibo","year":"2013","journal-title":"Wiley-IEEE Press"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.scitotenv.2015.02.090","article-title":"The influence of changes in wind patterns on the areal extension of surface cyanobacterial blooms in a large shallow lake in China","volume":"518","author":"Wu","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.jglr.2022.12.007","article-title":"Reporting on the status, trends, and drivers of algal blooms on Lake of the Woods using satellite-derived bloom indices (2002\u20132021)","volume":"49","author":"Binding","year":"2023","journal-title":"J. Great Lakes Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5291\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:19:47Z","timestamp":1760131187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,9]]},"references-count":40,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15225291"],"URL":"https:\/\/doi.org\/10.3390\/rs15225291","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,9]]}}}