{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T14:38:15Z","timestamp":1782398295329,"version":"3.54.5"},"reference-count":79,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002641","name":"Konkuk University","doi-asserted-by":"publisher","award":["2018"],"award-info":[{"award-number":["2018"]}],"id":[{"id":"10.13039\/501100002641","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)\u2014which has high temporal, spatial, and spectral resolutions\u2014is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7\u2013589.6, 603.6\u2013631.8, 641.2\u2013655.35, 664.8\u2013679.0, 698.0\u2013712.3, and 731.4\u2013784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments.<\/jats:p>","DOI":"10.3390\/rs14071754","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T21:08:22Z","timestamp":1649365702000},"page":"1754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0949-4735","authenticated-orcid":false,"given":"Wonjin","family":"Jang","sequence":"first","affiliation":[{"name":"Graduate School of Civil, Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1959-0843","authenticated-orcid":false,"given":"Yongeun","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Civil and Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JongCheol","family":"Pyo","sequence":"additional","affiliation":[{"name":"Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanghyun","family":"Park","sequence":"additional","affiliation":[{"name":"Water Quality Assessment Research Division, National Institute of Environmental Research, Incheon 22689, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7815-3400","authenticated-orcid":false,"given":"Jinuk","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Civil, Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Hwi","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Civil, Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung Hwa","family":"Cho","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Ki","family":"Shin","sequence":"additional","affiliation":[{"name":"Office for Busan Region Management of the Nakdong River, Korea Water Resources Corporation (K-Water), Busan 49300, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9729-9373","authenticated-orcid":false,"given":"Seongjoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Civil and Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.hal.2011.10.027","article-title":"The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change","volume":"14","author":"Davis","year":"2012","journal-title":"Harmful Algae"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1002\/etc.3220","article-title":"Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems?","volume":"35","author":"Brooks","year":"2016","journal-title":"Environ. Toxicol. Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.toxicon.2009.07.021","article-title":"The state of U.S. freshwater harmful algal blooms assessments, policy and legislation","volume":"55","author":"Hudnell","year":"2010","journal-title":"Toxicon"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/(ASCE)0733-9372(1997)123:7(714)","article-title":"Empirical Models for Disinfection By-Products in Lakes and Reservoirs","volume":"123","author":"Chapra","year":"1997","journal-title":"J. Environ. Eng."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"10078","DOI":"10.3390\/rs70810078","article-title":"Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu reservoir in Kinmen, Taiwan","volume":"7","author":"Su","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1093\/plankt\/fbq133","article-title":"An assessment of MERIS algal products during an intense bloom in Lake of the Woods","volume":"33","author":"Binding","year":"2011","journal-title":"J. Plankton Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"044009","DOI":"10.1088\/1748-9326\/5\/4\/044009","article-title":"A new three-band algorithm for estimating chlorophyll concentrations in turbid inland lakes","volume":"5","author":"Duan","year":"2010","journal-title":"Environ. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1016\/j.scib.2019.07.002","article-title":"Remote sensing of cyanobacterial blooms in inland waters: Present knowledge and future challenges","volume":"64","author":"Shi","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1080\/15481603.2014.900983","article-title":"Machine learning approaches to coastal water quality monitoring using GOCI satellite data","volume":"51","author":"Kim","year":"2014","journal-title":"GISci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0034-4257(02)00009-3","article-title":"Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data","volume":"81","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/S0048-9697(00)00682-3","article-title":"A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes","volume":"268","author":"Kutser","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1080\/01431160802552801","article-title":"Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary, USA","volume":"30","author":"Lunetta","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4147","DOI":"10.1080\/01431161003789549","article-title":"Using hyperspectral remote sensing to estimate chlorophyll-a and phycocyanin in a mesotrophic reservoir","volume":"31","author":"Li","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111350","DOI":"10.1016\/j.rse.2019.111350","article-title":"A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery","volume":"233","author":"Pyo","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pyo, J.C., Duan, H., Ligaray, M., Kim, M., Baek, S., Kwon, Y.S., Lee, H., Kang, T., Kim, K., and Cha, Y.K. (2020). An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery. Remote Sens., 12.","DOI":"10.3390\/rs12071073"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Keller, S., Maier, P.M., Riese, F.M., Norra, S., Holbach, A., B\u00f6rsig, N., Wilhelms, A., Moldaenke, C., Zaake, A., and Hinz, S. (2018). Hyperspectral data and machine learning for estimating CDOM, chlorophyll a, diatoms, green algae and turbidity. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15091881"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TFUZZ.2004.834810","article-title":"Input selection for nonlinear regression models","volume":"12","year":"2004","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_20","unstructured":"Dekker, A.G. (1993). Detection of Optical Water Quality Parameters for Eutrophic Waters by High Resolution Remote Sensing, Institute for Environmental Studies."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"758","DOI":"10.3390\/rs1040758","article-title":"A novel algorithm for predicting phycocyanin concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach","volume":"1","author":"Mishra","year":"2009","journal-title":"Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S0048-9697(00)00685-9","article-title":"Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons","volume":"268","author":"Kallio","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_24","first-page":"9","article-title":"Water area extraction using geocoded high resolution imagery of TerraSAR-X radar satellite in cloud prone Brahmaputra River valley","volume":"3","author":"Shafique","year":"2009","journal-title":"J. Geomat."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4774","DOI":"10.3390\/rs5104774","article-title":"A performance review of reflectance based algorithms for predicting phycocyanin concentrations in inland waters","volume":"5","author":"Ogashawara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_26","first-page":"153","article-title":"Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll pigments in eutrophic waters","volume":"55","author":"Schalles","year":"2000","journal-title":"Ergeb. Limnol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.oceano.2017.08.001","article-title":"Laboratory measurements of remote sensing reflectance of selected phytoplankton species from the Baltic Sea","volume":"60","author":"Darecki","year":"2018","journal-title":"Oceanologia"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2015.05.023","article-title":"Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission","volume":"167","author":"Hestir","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1016\/j.jglr.2018.05.004","article-title":"Phycocyanin concentration retrieval in inland waters: A comparative review of the remote sensing techniques and algorithms","volume":"44","author":"Yan","year":"2018","journal-title":"J. Great Lakes Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pyo, J.C., Ligaray, M., Kwon, Y.S., Ahn, M.H., Kim, K., Lee, H., Kang, T., Cho, S.B., Park, Y., and Cho, K.H. (2018). High-spatial resolution monitoring of phycocyanin and chlorophyll-a using airborne hyperspectral imagery. Remote Sens., 10.","DOI":"10.3390\/rs10081180"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and Van Den Bosch, J. (2014, January 24\u201327). MODTRAN\u00ae 6: A major upgrade of the MODTRAN\u00ae radiative transfer code. Proceedings of the Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, Lausanne, Switzerland.","DOI":"10.1117\/12.2050433"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1080\/2150704X.2016.1142680","article-title":"Chlorophyll- a concentration estimation using three difference bio-optical algorithms, including a correction for the low-concentration range: The case of the Yiam reservoir, Korea","volume":"7","author":"Pyo","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pyo, J.C., Pachepsky, Y., Baek, S.S., Kwon, Y.S., Kim, M.J., Lee, H., Park, S., Cha, Y.K., Ha, R., and Nam, G. (2017). Optimizing semi-analytical algorithms for estimating chlorophyll-a and phycocyanin concentrations in inland waters in Korea. Remote Sens., 9.","DOI":"10.3390\/rs9060542"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/S0032-9592(98)00153-8","article-title":"Phycocyanin from Spirulina sp.: Influence of processing of biomass on phycocyanin yield, analysis of efficacy of extraction methods and stability studies on phycocyanin","volume":"34","author":"Sarada","year":"1999","journal-title":"Process Biochem."},{"key":"ref_35","first-page":"1112","article-title":"A random forest class memberships based wrapper band selection criterion: Application to hyperspectral","volume":"2015","author":"Chehata","year":"2015","journal-title":"Int. Geosci. Remote Sens. Symp."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jaiswal, J.K., and Samikannu, R. (2017, January 2\u20134). Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression. Proceedings of the 2017 World Congress on Computing and Communication Technologies (WCCCT), Tiruchirappalli, India.","DOI":"10.1109\/WCCCT.2016.25"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2006.02.013","article-title":"Comparison of different satellite sensors in detecting cyanobacterial bloom events in the Baltic Sea","volume":"102","author":"Reinart","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/07438140609353895","article-title":"Influence of chlorophyll and colored dissolved organic matter (CDOM) on lake reflectance spectra: Implications for measuring lake properties by remote sensing","volume":"22","author":"Menken","year":"2006","journal-title":"Lake Reserv. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.rse.2014.04.033","article-title":"Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters","volume":"157","author":"Brezonik","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ha, N.T.T., Thao, N.T.P., Koike, K., and Nhuan, M.T. (2017). Selecting the best band ratio to estimate chlorophyll-a concentration in a tropical freshwater lake using sentinel 2A images from a case study of Lake Ba Be (Northern Vietnam). ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6090290"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1023\/A:1008143902418","article-title":"Comparative reflectance properties of algal cultures with manipulated densities","volume":"11","author":"Gitelson","year":"1999","journal-title":"J. Appl. Phycol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/01431169108929642","article-title":"The effect of suspended sediment on reflectance from freshwater algae","volume":"12","author":"Quibell","year":"1991","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression: A tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_44","first-page":"792","article-title":"Partial least square regression (PLS regression)","volume":"6","author":"Abdi","year":"2003","journal-title":"Encycl. Res. Methods Soc. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2010.08.007","article-title":"Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests","volume":"66","author":"Guo","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.oregeorev.2015.01.001","article-title":"Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines","volume":"71","year":"2015","journal-title":"Ore Geol. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1007\/s11269-019-02273-0","article-title":"A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction","volume":"33","author":"Shourian","year":"2019","journal-title":"Water Resour. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support vector machines","volume":"20","author":"Vapnik","year":"1995","journal-title":"Mach. Learn"},{"key":"ref_50","first-page":"199","article-title":"Application of support vector machine in power system","volume":"11","author":"Coulibaly","year":"2006","journal-title":"Study Dyn. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6208","DOI":"10.1007\/s11356-014-3806-7","article-title":"Prediction of water quality index in constructed wetlands using support vector machine","volume":"22","author":"Mohammadpour","year":"2015","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_52","first-page":"15","article-title":"Overview of artificial neural networks","volume":"458","author":"Zou","year":"2008","journal-title":"Methods Mol. Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1016\/j.marpolbul.2008.05.021","article-title":"An ANN application for water quality forecasting","volume":"56","author":"Palani","year":"2008","journal-title":"Mar. Pollut. Bull."},{"key":"ref_54","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1111\/j.1752-1688.2001.tb03630.x","article-title":"Validation of the swat model on a large rwer basin with point and nonpoint sources 1","volume":"37","author":"Santhi","year":"2001","journal-title":"JAWRA J. Am. Water Resour. Assoc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"885","DOI":"10.13031\/2013.23153","article-title":"Model evaluation guidelines for systematic quantification of accuracy in watershed simulations","volume":"50","author":"Moriasi","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1111\/j.1752-1688.2005.tb03740.x","article-title":"Hydrological modeling of the Iroquois River watershed using HSPF and SWAT","volume":"41","author":"Singh","year":"2005","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.3390\/rs2071722","article-title":"Why is the ratio of reflectivity effective for chlorophyll estimation in the lake water?","volume":"2","author":"Oki","year":"2010","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.rse.2003.10.014","article-title":"Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie","volume":"89","author":"Vincent","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1016\/j.watres.2011.02.002","article-title":"NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study","volume":"45","author":"Yacobi","year":"2011","journal-title":"Water Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"11689","DOI":"10.3390\/rs61211689","article-title":"Analysis of MERIS reflectance algorithms for estimating chlorophyll-a concentration in a Brazilian reservoir","volume":"6","author":"Ogashawara","year":"2014","journal-title":"Remote Sens."},{"key":"ref_63","first-page":"195","article-title":"Remote measurement of algal chlorophyll in surface waters: The case for the first derivative of reflectance near 690 nm","volume":"62","author":"Rundquist","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_64","first-page":"3619","article-title":"The influence of suspended clays on phytoplankton reflectance signatures and the remote estimation of chlorophyll","volume":"27","author":"Schalles","year":"2001","journal-title":"SIL Proc."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.earscirev.2020.103187","article-title":"Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing","volume":"205","author":"Sagan","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2014.06.008","article-title":"Remote quantification of phycocyanin in potable water sources through an adaptive model","volume":"95","author":"Song","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","unstructured":"Chang, N.-B., and Vannah, B. (2013, January 10\u201312). Intercomparisons between empirical models with data fusion techniques for monitoring water quality in a large lake. Proceedings of the 2013 10th IEEE International Conference on Networking, Sensing and Control (ICNSC 2013), Evry, France."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"115403","DOI":"10.1016\/j.watres.2019.115403","article-title":"Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy","volume":"171","author":"He","year":"2020","journal-title":"Water Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s12403-015-0175-5","article-title":"Developing a PCA\u2013ANN model for predicting chlorophyll a concentration from field hyperspectral measurements in dianshan lake, China","volume":"7","author":"Zhou","year":"2015","journal-title":"Water Qual. Expo. Health"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kim, S., Chung, S., Park, H., Cho, Y., and Lee, H. (2019). Analysis of environmental factors associated with cyanobacterial dominance after river weir installation. Water, 11.","DOI":"10.3390\/w11061163"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1128\/aem.36.4.572-576.1978","article-title":"Effect of temperature on blue-green algae (Cyanobacteria) in Lake Mendota","volume":"36","author":"Konopka","year":"1978","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1111\/j.1365-2427.2012.02866.x","article-title":"Comparison of cyanobacterial and green algal growth rates at different temperatures","volume":"58","author":"Eshetu","year":"2013","journal-title":"Freshw. Biol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.algal.2018.09.018","article-title":"Temperature effects on growth rates and fatty acid content in freshwater algae and cyanobacteria","volume":"35","author":"Nalley","year":"2018","journal-title":"Algal Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1016\/j.watres.2011.08.002","article-title":"Climate change: Links to global expansion of harmful cyanobacteria","volume":"46","author":"Paerl","year":"2012","journal-title":"Water Res."},{"key":"ref_75","first-page":"103","article-title":"Factors Affecting Growth of Cyanobacteria","volume":"59","author":"Berg","year":"2015","journal-title":"Monaldi Arch. Chest Dis. Pulm. Ser."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"150423","DOI":"10.1016\/j.scitotenv.2021.150423","article-title":"Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data","volume":"805","author":"Dev","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.watres.2017.09.026","article-title":"Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea","volume":"126","author":"Park","year":"2017","journal-title":"Water Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1071\/MF97039","article-title":"Microcystis bloom formation in the lower Nakdong River, South Korea: Importance of hydrodynamics and nutrient loading","volume":"50","author":"Ha","year":"1999","journal-title":"Mar. Freshw. Res."},{"key":"ref_79","unstructured":"Oliver, R.L., and Ganf, G.G. (2000). Freshwater blooms. The Ecology of Cyanobacteria, Springer."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1754\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:49:14Z","timestamp":1760136554000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,6]]},"references-count":79,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071754"],"URL":"https:\/\/doi.org\/10.3390\/rs14071754","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,6]]}}}