{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T12:44:51Z","timestamp":1768826691906,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"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>A record-breaking agglomeration of Sargassum was packed along the northern Jeju coast in Korea in 2021, and laborers suffered from removing them from the beach. If remote sensing can be used to detect the locations at which Sargassum accumulated in a timely and accurate manner, we could remove them before their arrival and reduce the damage caused by Sargassum. This study aims to detect Sargassum distribution on the coast of Jeju Island using the Geostationary KOMPSAT 2B (GK2B) Geostationary Ocean Color Imager-II (GOCI-II) imagery that was launched in February 2020, with measurements available since October 2020. For this, we used GOCI-II imagery during the first 6 months and machine learning models including Fine Tree, a Fine Gaussian support vector machine (SVM), and Gentle adaptive boosting (GentleBoost). We trained the models with the GOCI-II Rayleigh-corrected reflectance (RhoC) image and a ground truth map extracted from high-resolution images as input and output, respectively. Qualitative and quantitative assessments were carried out using the three machine learning models and traditional methods such as Sargassum indexes. We found that GentleBoost showed a lower false positive (6.2%) and a high F-measure level (0.82), and a more appropriate Sargassum distribution compared to other methods. The application of the machine learning model to GOCI-II images in various atmospheric conditions is therefore considered successful for mapping Sargassum extent quickly, enabling reduction of laborers\u2019 efforts to remove them.<\/jats:p>","DOI":"10.3390\/rs13234844","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Sargassum Detection Using Machine Learning Models: A Case Study with the First 6 Months of GOCI-II Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0700-1175","authenticated-orcid":false,"given":"Jisun","family":"Shin","sequence":"first","affiliation":[{"name":"BK21 School of Earth and Environmental Systems, Pusan National University, 2, Busandaehak-ro 63 beon-gil, Busan 46241, Korea"}]},{"given":"Jong-Seok","family":"Lee","sequence":"additional","affiliation":[{"name":"BK21 School of Earth and Environmental Systems, Pusan National University, 2, Busandaehak-ro 63 beon-gil, Busan 46241, Korea"}]},{"given":"Lee-Hyun","family":"Jang","sequence":"additional","affiliation":[{"name":"LION PLUS Corp., 38, Jungang-daero 1367 beon-gil, Busan 47728, Korea"}]},{"given":"Jinwook","family":"Lim","sequence":"additional","affiliation":[{"name":"LION PLUS Corp., 38, Jungang-daero 1367 beon-gil, Busan 47728, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-7990","authenticated-orcid":false,"given":"Boo-Keun","family":"Khim","sequence":"additional","affiliation":[{"name":"BK21 School of Earth and Environmental Systems, Pusan National University, 2, Busandaehak-ro 63 beon-gil, Busan 46241, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8013-998X","authenticated-orcid":false,"given":"Young-Heon","family":"Jo","sequence":"additional","affiliation":[{"name":"BK21 School of Earth and Environmental Systems, Pusan National University, 2, Busandaehak-ro 63 beon-gil, Busan 46241, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3894","DOI":"10.1111\/j.1365-294X.2011.05220.x","article-title":"Phylogeographic heterogeneity of the brown macroalga Sargassum horneri (Fucaceae) in the northwestern Pacific in relation to late Pleistocene glaciation and tectonic configurations","volume":"20","author":"Hu","year":"2011","journal-title":"Mol. Ecol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1080\/00318884.2019.1585722","article-title":"An increase in new Sargassum (Phaeophyceae) blooms along the coast of the East China Sea and Yellow Sea","volume":"58","author":"Zhang","year":"2019","journal-title":"Phycologia"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111845","DOI":"10.1016\/j.marpolbul.2020.111845","article-title":"Sargassum blooms in the East China Sea and Yellow Sea","volume":"162","author":"Zhuang","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11501","DOI":"10.1002\/2017GL075525","article-title":"Floating algae blooms in the East China Sea","volume":"44","author":"Qi","year":"2017","journal-title":"Geophy. Res. Lett."},{"key":"ref_5","unstructured":"(2021, October 30). Press Release Provided by the Ministry of Oceans and Fisheries in Korea. Available online: https:\/\/www.korea.kr\/news\/pressReleaseView.do?newsId=156448342."},{"key":"ref_6","first-page":"1","article-title":"Development and implementation of Sargassum early advisory system (SEAS)","volume":"81","author":"Webster","year":"2013","journal-title":"Shore Beach"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"317","DOI":"10.2112\/SI90-040.1","article-title":"Long-term trend of green and golden tide in the eastern Yellow Sea","volume":"SI90","author":"Kim","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1080\/01431161003639660","article-title":"Distribution of floating Sargassum in the Gulf of Mexico and the Atlantic Ocean mapped using MERIS","volume":"32","author":"Gower","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5669","DOI":"10.1080\/01431161.2019.1658240","article-title":"The distribution of pelagic Sargassum observed with OLCI","volume":"41","author":"Gower","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2016.04.019","article-title":"Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations","volume":"183","author":"Wang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3852","DOI":"10.1080\/01431161.2018.1447161","article-title":"On the continuity of quantifying floating algae of the Central West Atlantic between MODIS and VIIRS","volume":"39","author":"Wang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1126\/science.aaw7912","article-title":"The great Atlantic Sargassum belt","volume":"365","author":"Wang","year":"2019","journal-title":"Science"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1109\/TGRS.2006.882258","article-title":"Ocean Color Satellites Show Extensive Lines of Floating Sargassum in the Gulf of Mexico","volume":"44","author":"Gower","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gower, J., and King, S. (2008). Satellite images show the movement of floating Sargassum in the Gulf of Mexico and Atlantic Ocean. Nat. Prec.","DOI":"10.1038\/npre.2008.1894.1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1080\/2150704X.2013.796433","article-title":"Satellite images suggest a new Sargassum source region in 2011","volume":"4","author":"Gower","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2016.02.065","article-title":"Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique","volume":"178","author":"Xing","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"113","article-title":"High-resolution satellite observations of a new hazard of golden tides caused by floating Sargassum in winter in the Yellow Sea","volume":"178","author":"Xing","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3646","DOI":"10.1109\/JSTARS.2018.2863194","article-title":"Characterizing a sea turtle developmental habitat using Landsat observations of surface-pelagic drift communities in the eastern Gulf of Mexico","volume":"11","author":"Hardy","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.rse.2018.07.014","article-title":"Coral reef applications of Sentinel-2: Coverage, characteristics, bathymetry and benthic mapping with comparison to Landsat 8","volume":"216","author":"Hedley","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1029\/2016EO058355","article-title":"Sargassum watch warns of incoming seaweed","volume":"97","author":"Hu","year":"2016","journal-title":"Eos"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1109\/TGRS.2020.3002929","article-title":"Automatic Extraction of Sargassum Features from Sentinel-2 MSI Images","volume":"59","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"302","DOI":"10.2112\/SI90-038.1","article-title":"U-Net convolutional neural network model for deep red tide learning using GOCI","volume":"SI90","author":"Kim","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"26810","DOI":"10.1364\/OE.26.026810","article-title":"Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: Case study of Yellow Sea using GOCI images","volume":"26","author":"Qiu","year":"2018","journal-title":"Opt. Express"},{"key":"ref_25","first-page":"202","article-title":"Machine learning approaches for quantifying Margalefidinium polykrikoides bloom from airborne hyperspectral imagery","volume":"90","author":"Shin","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shin, J., Jo, Y.H., Ryu, J.H., Khim, B.K., and Kim, S.M. (2021). High spatial red tide detection in the Southern Coast of Korea using U-Net from PlanetScope imagery. Sensors, 21.","DOI":"10.3390\/s21134447"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.neucom.2018.06.088","article-title":"Accurate Ulva prolifera regions extraction of UAV images with superpixel and CNNs for ocean environment monitoring","volume":"348","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Minsky, M., and Papert, S.A. (2017). Perceptrons: An Introduction to Computational Geometry, MIT Press.","DOI":"10.7551\/mitpress\/11301.001.0001"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","article-title":"A survey on deep learning for big data","volume":"42","author":"Zhang","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3608","DOI":"10.1080\/01431161.2018.1447162","article-title":"A satellite remote-sensing multi-index approach to discriminate pelagic Sargassum in the waters of the Yucatan Peninsula, Mexico","volume":"39","author":"Cuevas","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wan, J., Zhang, J., Zhao, J., Ye, F., Wang, Z., and Liu, S. (August, January 28). Automatic Extraction Method of Sargassum Based on Spectral-Texture Features of Remote Sensing Images. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898131"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e6842","DOI":"10.7717\/peerj.6842","article-title":"ERISNet: Deep neural network for Sargassum detection along the coastline of the Mexican Caribbean","volume":"7","year":"2019","journal-title":"PeerJ"},{"key":"ref_33","first-page":"325","article-title":"A review of the yellow sea circulation models","volume":"20","author":"Kim","year":"1998","journal-title":"Ocean Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1175\/1520-0485(1986)016<0241:WWACSL>2.0.CO;2","article-title":"Wintertime winds and coastal sealevel fluctuations in the northeast china sea. Part II: Numerical model","volume":"16","author":"Hsueh","year":"1986","journal-title":"J. Phys. Oceanogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/S0278-4343(00)00102-3","article-title":"Seasonal mean circulation in the Yellow Sea\u2014A model-generated climatology","volume":"21","author":"Naimie","year":"2001","journal-title":"Cont. Shelf Res."},{"key":"ref_36","unstructured":"(2021, October 30). Korea Hydrographic and Oceanographic Agency (KHOA). Available online: http:\/\/khoa.go.kr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1016\/j.rse.2009.05.012","article-title":"A novel ocean color index to detect floating algae in the global oceans","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111659","DOI":"10.1016\/j.rse.2020.111659","article-title":"In search of floating algae and other organisms in global oceans and lakes","volume":"239","author":"Qi","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"(2021, October 30). Korea Ocean Satellite Center (KOSC). Available online: http:\/\/kosc.kiost.ac.kr."},{"key":"ref_40","unstructured":"U.S. Geological Survey (2021, October 30). Available online: http:\/\/glovis.usgs.gov."},{"key":"ref_41","unstructured":"(2021, October 30). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7442","DOI":"10.1364\/AO.38.007442","article-title":"Estimation of the remote-sensing reflectance from above-surface measurements","volume":"38","author":"Mobley","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_43","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogram. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"310","DOI":"10.2112\/SI90-039.1","article-title":"Reflectivity characteristics of the green and golden tides from the Yellow Sea and East China Sea","volume":"SI90","author":"Min","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2015.01.027","article-title":"Hyperspectral discrimination of floating mats of seagrass wrack and the macroalgae Sargassum in coastal waters of Greater Florida Bay using airborne remote sensing","volume":"167","author":"Dierssen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_47","first-page":"771","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"J. Jpn. Soc. Artif. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors)","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1007442505281","article-title":"Glossary of terms","volume":"30","author":"Kohavi","year":"1998","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/j.1529-8817.2007.00422.x","article-title":"Bio optical characteristics of PSII and PSI in 33 species (13 pigment groups) of marine phytoplankton, and the relevance for pulse amplitude-modulated and fast-repetition-rate fluorometry1","volume":"43","author":"Johnsen","year":"2007","journal-title":"J. Phycol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s003380050055","article-title":"In vivo absorbance spectra and the ecophysiology of reef macroalgae","volume":"16","author":"Beach","year":"1997","journal-title":"Coral Reefs"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1111\/j.0022-3646.1997.00408.x","article-title":"The significance of intracellular self-shading on the bio-optical properties of brown, red, and green macroalgae","volume":"33","author":"Grzymski","year":"1997","journal-title":"J. Phycol."},{"key":"ref_54","first-page":"102","article-title":"Chlorophyll concentration derived from microwave remote sensing measurements using artificial neural network algorithms","volume":"26","author":"Jo","year":"2018","journal-title":"J. Mar. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"102001","DOI":"10.1016\/j.hal.2021.102001","article-title":"To what extent can Ulva and Sargassum be detected and separated in satellite imagery?","volume":"103","author":"Qi","year":"2021","journal-title":"Harmful Algae"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1029\/2008EO330002","article-title":"Origin and offshore extent of floating algae in Olympic sailing area","volume":"89","author":"Hu","year":"2008","journal-title":"Eos Trans. AGU"},{"key":"ref_57","first-page":"295","article-title":"MODIS vegetation index (MOD13)","volume":"3","author":"Huete","year":"1999","journal-title":"Algorithm Theor. Basis Doc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","article-title":"An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization","volume":"40","author":"Dietterich","year":"2000","journal-title":"Mach. Learn."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4844\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:37:22Z","timestamp":1760168242000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,29]]},"references-count":58,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234844"],"URL":"https:\/\/doi.org\/10.3390\/rs13234844","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,29]]}}}