{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T07:33:47Z","timestamp":1768462427983,"version":"3.49.0"},"reference-count":100,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["06579-2014"],"award-info":[{"award-number":["06579-2014"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["448172-2014"],"award-info":[{"award-number":["448172-2014"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-a) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-a retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-a were built for each OWT. The performances of chl-a retrieval algorithms within each OWT were compared to those of global chl-a algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-a:turbidity ratio; turbid lakes with a mixture of high chl-a and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-a and turbidity measurements; and eutrophic lakes with a high chl-a:turbidity ratio. With one exception (r2 = 0.26, p = 0.08), the best performing algorithm in each OWT showed improvement (r2 = 0.69\u20130.91, p &lt; 0.05), compared with the best performing algorithm for all lakes combined (r2 = 0.52, p &lt; 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-a over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes.<\/jats:p>","DOI":"10.3390\/rs13224607","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T02:42:28Z","timestamp":1637116948000},"page":"4607","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4164-7084","authenticated-orcid":false,"given":"Michael A.","family":"Dallosch","sequence":"first","affiliation":[{"name":"Department of Biology, Western University, London, ON N6A 3K7, Canada"}]},{"given":"Irena F.","family":"Creed","sequence":"additional","affiliation":[{"name":"Department of Biology, Western University, London, ON N6A 3K7, Canada"},{"name":"Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"HABs in a changing world: A perspective on harmful algal blooms, their impacts, and research and management in a dynamic era of climactic and environmental change","volume":"Volume 2012","author":"Kim","year":"2012","journal-title":"Harmful Algae 2012: Proceedings of the 15th International Conference on Harmful Algae: 2012, CECO, Changwon, Gyeongnam, Korea"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1139\/cjfas-2015-0470","article-title":"Blooming algae: A Canadian perspective on the rise of toxic cyanobacteria","volume":"73","author":"Pick","year":"2016","journal-title":"Can. J. Fish Aquat. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/07438141.2011.557765","article-title":"Algal blooms in Ontario, Canada: Increases in reports since 1994","volume":"27","author":"Winter","year":"2011","journal-title":"Lake Reserv. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/brv.12480","article-title":"Emerging threats and persistent conservation challenges for freshwater biodiversity","volume":"94","author":"Reid","year":"2019","journal-title":"Biol. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10750-016-3007-0","article-title":"The importance of small waterbodies for biodiversity and ecosystem services: Implications for policy makers","volume":"793","author":"Biggs","year":"2017","journal-title":"Hydrobiologia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.4319\/lo.2009.54.6_part_2.2283","article-title":"Lakes as sentinels of climate change","volume":"54","author":"Adrian","year":"2009","journal-title":"Limnol. Oceanogr."},{"key":"ref_7","first-page":"1","article-title":"The vulnerability of lakes to climate change along an altitudinal gradient","volume":"2","author":"Medhaug","year":"2021","journal-title":"Commun. Earth Env."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Suthers, I.M., Rissik, D.S., and Richardson, A. (2019). Plankton: A Guide to Their Ecology and Monitoring for Water Quality, CRC Press, Taylor & Francis Group. [2nd ed.].","DOI":"10.1071\/9781486308804"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6855","DOI":"10.1080\/01431161.2010.512947","article-title":"A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters","volume":"32","author":"Matthews","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ogashawara, I., Mishra, D.R., and Gitelson, A.A. (2017). Remote Sensing of Inland Waters: Background and Current State-of-the-Art. Bio-Optical Modeling and Remote Sensing of Inland Waters, Elsevier.","DOI":"10.1016\/B978-0-12-804644-9.00001-X"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431160310001592445","article-title":"On the potential of MODIS and MERIS for imaging chlorophyll fluorescence from space","volume":"25","author":"Gower","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2016.04.015","article-title":"The impact of improved signal-to-noise ratios on algorithm performance: Case studies for Landsat class instruments","volume":"185","author":"Schott","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","unstructured":"Boland, D.H.P. (1975). Trophic classification of lakes using Landsat-1 (ERTS-1) multispectral scanner data. U.S, Environmental Protection Agency, Assessment and Criteria Development Division Corvallis Environmental Research Laboratory."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/BF00018955","article-title":"Chlorophyll differences in Mono Lake (California) observable on Landsat imagery","volume":"122","author":"Almanza","year":"1985","journal-title":"Hydrobiologia"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(90)90039-O","article-title":"The relationship of MSS and TM digital data with suspended sediments, chlorophyll, and temperature in Moon Lake, Mississippi","volume":"33","author":"Ritchie","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/01431169508954386","article-title":"Chlorophyll distribution in lake Kinneret determined from Landsat Thematic Mapper data","volume":"16","author":"Mayo","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1080\/014311601450059","article-title":"Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images","volume":"22","author":"Brivio","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10661-006-9362-y","article-title":"Assessment of chlorophyll-a concentration and trophic state for Lake Chagan using Landsat TM and field spectral data","volume":"129","author":"Duan","year":"2007","journal-title":"Environ. Monit. Assess."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"67","DOI":"10.4236\/jwarp.2011.31008","article-title":"Spectral geometric triangle properties of chlorophyll-a inversion in Taihu Lake based on TM data","volume":"3","author":"Chen","year":"2011","journal-title":"J. Water Resour. Prot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.5194\/isprsarchives-XL-7-W3-1511-2015","article-title":"Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?","volume":"40","author":"Theologou","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"02003","DOI":"10.1051\/e3sconf\/202014302003","article-title":"An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data","volume":"Volume 143","author":"Chen","year":"2020","journal-title":"E3S Web of Conferences"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Paltsev, A., and Creed, I.F. (2021). Are Northern Lakes in Relatively Intact Temperate Forests Showing Signs of Increasing Phytoplankton Biomass?. Ecosystems, 1\u201329.","DOI":"10.1007\/s10021-021-00684-y"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1016\/S0273-1177(03)00365-X","article-title":"Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a","volume":"33","author":"Carder","year":"2004","journal-title":"Adv. Space Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24937","DOI":"10.1029\/98JC02160","article-title":"Ocean color chlorophyll algorithms for SeaWiFS","volume":"103","author":"Maritorena","year":"1998","journal-title":"J. Geophys. Res.-Oceans."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Salem, S.I., Higa, H., Kim, H., Kobayashi, H., Oki, K., and Oki, T. (2017). Assessment of chlorophyll-a algorithms considering different trophic statuses and optimal bands. Sensors, 17.","DOI":"10.3390\/s17081746"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1111\/j.1752-1688.2006.tb06029.x","article-title":"Lake water quality assessment from Landsat thematic mapper data using neural network: An approach to optimal band combination selection","volume":"42","author":"Sudheer","year":"2006","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5245","DOI":"10.1080\/01431160500219182","article-title":"Estimating and mapping chlorophyll a concentration in Pensacola Bay, Florida using Landsat ETM data","volume":"26","author":"Han","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2006.12.010","article-title":"Understanding variation in trophic status of lakes on the Boreal Plain: A 20 year retrospective using Landsat TM imagery","volume":"109","author":"Sass","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/07438140509354442","article-title":"Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM)","volume":"21","author":"Brezonik","year":"2005","journal-title":"Lake Reserv. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ha, N., Thao, N., Koike, K., and Nhuan, M. (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_31","doi-asserted-by":"crossref","first-page":"045005","DOI":"10.1088\/1748-9326\/4\/4\/045005","article-title":"Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data\u2014successes and challenges","volume":"4","author":"Moses","year":"2009","journal-title":"Environ. Res. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2016.01.007","article-title":"Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes","volume":"185","author":"Olmanson","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Richardson, L., and LeDrew, E. (2006). Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal. Remote Sensing of Aquatic Coastal Ecosystem Processes, Springer.","DOI":"10.1007\/1-4020-3968-9"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.rse.2007.01.016","article-title":"Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study","volume":"109","author":"Gitelson","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dall\u2019Olmo, G., Gitelson, A.A., and Rundquist, D.C. (2003). Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters. Geophys. Res. Lett., 30.","DOI":"10.1029\/2003GL018065"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1080\/01431169408954353","article-title":"The spectral responses of algal chlorophyll in water with varying levels of suspended sediment","volume":"15","author":"Han","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","first-page":"3553","article-title":"Blue-red-NIR model for chlorophyll-\u03b1 retrieval in hypersaline-alkaline water using Landsat ETM+ sensor","volume":"7","author":"Singh","year":"2014","journal-title":"IEEE J. Sel. Top. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"024023","DOI":"10.1088\/1748-9326\/6\/2\/024023","article-title":"Estimation of chlorophyll-a concentration in productive turbid waters using a Hyperspectral Imager for the Coastal Ocean\u2014the Azov Sea case study","volume":"6","author":"Gitelson","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1080\/01431161.2018.1430912","article-title":"Monitoring algal blooms in drinking water reservoirs using the Landsat-8 operational land imager","volume":"39","author":"Keith","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1080\/01431161.2017.1404164","article-title":"Effects of sediments and coloured dissolved organic matter on remote sensing of chlorophyll-a using Landsat TM\/ETM+ over turbid waters","volume":"39","author":"Lin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2016.04.011","article-title":"Landsat 8: Providing continuity and increased precision for measuring multi-decadal time series of total suspended matter","volume":"185","author":"Lymburner","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ma, J., Song, K., Wen, Z., Zhao, Y., Shang, Y., Fang, C., and Du, J. (2016). Spatial distribution of diffuse attenuation of photosynthetic active radiation and its main regulating factors in Inland Waters of Northeast China. Remote Sens., 8.","DOI":"10.3390\/rs8110964"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1764","DOI":"10.1109\/36.942555","article-title":"A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms","volume":"39","author":"Moore","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1016\/j.rse.2009.07.016","article-title":"A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product","volume":"113","author":"Moore","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2017.03.036","article-title":"An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications","volume":"203","author":"Jackson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1002\/lno.10674","article-title":"Optical types of inland and coastal waters","volume":"63","author":"Spyrakos","year":"2018","journal-title":"Limnol. Oceanogr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.rse.2013.11.021","article-title":"An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters","volume":"143","author":"Moore","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and classification of hyperspectral images using watershed transformation","volume":"43","author":"Tarabalka","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.1016\/j.rse.2007.12.013","article-title":"A 20-year landsat water clarity census of Minnesota\u2019s 10,000 lakes","volume":"112","author":"Olmanson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3582","DOI":"10.1364\/AO.39.003582","article-title":"Atmospheric correction of satellite ocean color imagery: The black pixel assumption","volume":"39","author":"Siegel","year":"2000","journal-title":"Appl. Opt."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ilori, C.O., Pahlevan, N., and Knudby, A. (2019). Analyzing performances of different atmospheric correction techniques for Landsat 8: Application for coastal remote sensing. Remote Sens., 11.","DOI":"10.3390\/rs11040469"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bi, S., Li, Y., Wang, Q., Lyu, H., Liu, G., Zheng, Z., Du, C., Mu, M., Xu, J., and Lei, S. (2018). Inland water atmospheric correction based on turbidity classification using OLCI and SLSTR synergistic observations. Remote Sens., 10.","DOI":"10.3390\/rs10071002"},{"key":"ref_54","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_55","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_56","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.ecolind.2016.04.020","article-title":"A novel MODIS algorithm to estimate chlorophyll a concentration in eutrophic turbid lakes","volume":"69","author":"Zhang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_57","first-page":"4430","article-title":"A Hybrid EOF Algorithm to Improve MODIS Cyanobacteria Phycocyanin Data Quality in a Highly Turbid Lake: Bloom and Nonbloom Condition","volume":"10","author":"Tao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111604","DOI":"10.1016\/j.rse.2019.111604","article-title":"Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach","volume":"240","author":"Pahlevan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1109\/TGRS.2003.818464","article-title":"Revised Landsat-5 TM radiometric calibration procedures and post calibration dynamic ranges","volume":"41","author":"Chander","year":"2003","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1080\/01431169408954228","article-title":"An atmospheric correction method for the automatic retrieval of surface reflectances from TM images","volume":"15","author":"Gilabert","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","unstructured":"Chandrasekhar, S. (1960). Radiative Transfer, Dover Publications."},{"key":"ref_62","unstructured":"Vermote, E., Tanr\u00e9, D., Deuz\u00e9, J.L., Herman, M., Morcrette, J.J., and Kotchenova, S.Y. (2006). Second Simulation of a Satellite Signal in the Solar Spectrum-Vector (6SV), Laboratoire d\u2019Optique Atmosph\u00e9rique. 6s User Guide Version 3."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.1364\/AO.19.003427","article-title":"Revised depolarization corrections for atmospheric extinction","volume":"19","author":"Young","year":"1980","journal-title":"Appl. Opt."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1364\/AO.34.002765","article-title":"Rayleigh-scattering calculations for the terrestrial atmosphere","volume":"34","author":"Bucholtz","year":"1995","journal-title":"Appl. Opt."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1854","DOI":"10.1175\/1520-0426(1999)016<1854:ORODC>2.0.CO;2","article-title":"On Rayleigh optical depth calculations","volume":"16","author":"Bodhaine","year":"1999","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/BF00168069","article-title":"Light scattering in planetary atmospheres","volume":"16","author":"Hansen","year":"1974","journal-title":"Space Sci. Rev."},{"key":"ref_67","unstructured":"Cracknell, A.P. (1981). The atmospheric correction of remotely sensed data and the quantitative determination of suspended matter in marine water surface layers. Remote Sensing in Meteorology, Oceanography and Hydrology, Ellis Horwood Limited. Chapter 11."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Jorge, D., Barbosa, C., De Carvalho, L., Affonso, A.G., Lobo, F., and Novo, E. (2017). SNR (signal-to-noise ratio) impact on water constituent retrieval from simulated images of optically complex amazon lakes. Remote Sens., 9.","DOI":"10.3390\/rs9070644"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"12503","DOI":"10.3390\/rs70912503","article-title":"Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network","volume":"7","author":"Jones","year":"2015","journal-title":"Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"DeVries, B., Huang, C., Lang, M., Jones, J., Huang, W., Creed, I., and Carroll, M. (2017). Automated quantification of surface water inundation in wetlands using optical satellite imagery. Remote Sens., 9.","DOI":"10.3390\/rs9080807"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2012.11.006","article-title":"Remote Sensing of Environment Extraction of remote sensing-based forest management units in tropical forests","volume":"130","author":"Hou","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1007\/s11554-020-00990-z","article-title":"Real time segmentation of remote sensing images with a combination of clustering and Bayesian approaches","volume":"18","author":"Song","year":"2020","journal-title":"J. Real Time Image Process."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10201-020-00633-z","article-title":"Optical water types found in Brazilian waters","volume":"22","author":"Barbosa","year":"2021","journal-title":"Limnology"},{"key":"ref_74","unstructured":"Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., and Team, R.C. (2021). Package \u2018caret\u2019. R J., 223. Available online: https:\/\/github.com\/topepo\/caret\/."},{"key":"ref_75","unstructured":"Kassambara, A., and Mundt, F. (2021, November 11). Package \u2018factoextra\u2019. Extract and Visualize the Results of Multivariate Data Analyses. Available online: http:\/\/www.sthda.com\/english\/rpkgs\/factoextra."},{"key":"ref_76","unstructured":"Mahto, A. (2021, November 11). Package \u2018splitstackshape\u2019. Available online: https:\/\/github.com\/topepo\/caret\/."},{"key":"ref_77","first-page":"113","article-title":"Package \u2018mass\u2019","volume":"538","author":"Ripley","year":"2013","journal-title":"Cran R"},{"key":"ref_78","first-page":"7","article-title":"Package \u2018lmtest\u2019. Testing linear regression models","volume":"2","author":"Hothorn","year":"2002","journal-title":"Cran R"},{"key":"ref_79","unstructured":"Hamner, B., Frasco, M., and LeDell, E. (2021, November 11). Metrics: Evaluation Metrics for Machine Learning. Available online: https:\/\/github.com\/mfrasco\/Metrics."},{"key":"ref_80","unstructured":"Yan, Y. (2021, November 11). MLmetrics: Machine Learning Evaluation Metrics. Available online: http:\/\/github.com\/yanyachen\/MLmetrics\/issues."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10750-010-0198-7","article-title":"Partitioning particulate scattering and absorption into contributions of phytoplankton and non-algal particles in winter in Lake Taihu (China)","volume":"644","author":"Sun","year":"2010","journal-title":"Hydrobiologia"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/lom3.10074","article-title":"A quantitative blueness index for oligotrophic waters: Application to Lake Tahoe, California-Nevada","volume":"14","author":"Watanabe","year":"2016","journal-title":"Limnol. Oceanogr."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.rse.2019.01.023","article-title":"Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity","volume":"224","author":"Kuhn","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2011WR011005","article-title":"Evaluation of medium to low resolution satellite imagery for regional lake water quality assessments","volume":"47","author":"Olmanson","year":"2011","journal-title":"Water Resour. Res."},{"key":"ref_85","first-page":"701","article-title":"Spectral reflectance with varying suspended sediment concentrations in clear and algae-laden waters","volume":"63","author":"Han","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Pahlevan, N., Balasubramanian, S.V., Sarkar, S., and Franz, B.A. (2018). Toward long-term aquatic science products from heritage Landsat missions. Remote Sens., 10.","DOI":"10.3390\/rs10091337"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Uudeberg, K., Ansko, I., P\u00f5ru, G., Ansper, A., and Reinart, A. (2019). Using optical water types to monitor changes in optically complex inland and coastal waters. Remote Sens., 11.","DOI":"10.3390\/rs11192297"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Mascarenhas, V., and Keck, T. (2019). Marine Optics and Ocean Color Remote Sensing. YOUMARES 8\u2013Oceans across Boundaries: Learning from Each Other, Springer.","DOI":"10.1007\/978-3-319-93284-2_4"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.marpolbul.2016.02.076","article-title":"The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)","volume":"107","author":"Slonecker","year":"2016","journal-title":"Mar. Pollut. Bull."},{"key":"ref_90","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_91","doi-asserted-by":"crossref","unstructured":"D\u00f6rnh\u00f6fer, K., G\u00f6ritz, A., Gege, P., Pflug, B., and Oppelt, N. (2016). Water constituents and water depth retrieval from Sentinel-2A\u2014A first evaluation in an oligotrophic lake. Remote Sens., 8.","DOI":"10.3390\/rs8110941"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Maier, P.M., Keller, S., and Hinz, S. (2021). Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies. Remote Sens., 13.","DOI":"10.3390\/rs13040718"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"67","DOI":"10.5194\/isprs-archives-XLII-3-W11-67-2020","article-title":"Optmization of Bio-Optical Model Parameters for Turbid Lake Water Quality Estimation Using Landsat 8 and WASI-2D","volume":"42","author":"Manuel","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"747309","DOI":"10.1117\/12.830630","article-title":"New algorithm for MODIS chlorophyll fluorescence height retrieval: Performance and comparison with the current product","volume":"7473","author":"Ioannou","year":"2009","journal-title":"Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2009"},{"key":"ref_95","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_96","doi-asserted-by":"crossref","first-page":"10694","DOI":"10.3390\/rs61110694","article-title":"An EOF-based algorithm to estimate chlorophyll a concentrations in Taihu Lake from MODIS land-band measurements: Implications for near real-time applications and forecasting models","volume":"6","author":"Qi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Mansaray, A.S., Dzialowski, A.R., Martin, M.E., Wagner, K.L., Gholizadeh, H., and Stoodley, S.H. (2021). Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds. Remote Sens., 13.","DOI":"10.3390\/rs13091847"},{"key":"ref_98","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."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1364\/JOSAA.34.000523","article-title":"Transformation of a high-dimensional color space for material classification","volume":"34","author":"Liu","year":"2017","journal-title":"J. Opt. Soc. Am."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"140","DOI":"10.3389\/fmars.2017.00140","article-title":"The OLCI Neural Network Swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters","volume":"4","author":"Hieronymi","year":"2017","journal-title":"Front. Mar. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4607\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:03Z","timestamp":1760167863000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,16]]},"references-count":100,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224607"],"URL":"https:\/\/doi.org\/10.3390\/rs13224607","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,16]]}}}