{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:29:25Z","timestamp":1773818965033,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"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>Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.<\/jats:p>","DOI":"10.3390\/rs12203278","type":"journal-article","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T10:19:23Z","timestamp":1602238763000},"page":"3278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1298-4839","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Geography &amp; Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2066-0498","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-9343","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA"}]},{"given":"Hongxing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA"}]},{"given":"Richard","family":"Beck","sequence":"additional","affiliation":[{"name":"Department of Geography and GIScience, University of Cincinnati, Cincinnati, OH 45221, USA"}]},{"given":"Molly","family":"Reif","sequence":"additional","affiliation":[{"name":"U.S. Army Corps of Engineers, ERDC, JALBTCX, Kiln, MS 39556, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0152-0107","authenticated-orcid":false,"given":"Erich","family":"Emery","sequence":"additional","affiliation":[{"name":"U.S. Army Corps of Engineers, Great Lakes and Ohio River Division, Cincinnati, OH 45202, USA"}]},{"given":"Jade","family":"Young","sequence":"additional","affiliation":[{"name":"U.S. Army Corps of Engineers, Louisville District, Water Quality, Louisville, KY 40202, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-4073","authenticated-orcid":false,"given":"Qiusheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Tennessee, Knoxville, TN 37996, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,9]]},"reference":[{"key":"ref_1","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. 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