{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T05:30:39Z","timestamp":1776749439355,"version":"3.51.2"},"reference-count":87,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"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 (NSERC)","doi-asserted-by":"publisher","award":["05265-2019"],"award-info":[{"award-number":["05265-2019"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","doi-asserted-by":"publisher","award":["EDF-CA-2021i023"],"award-info":[{"award-number":["EDF-CA-2021i023"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008638","name":"Environment and Climate Change Canada Climate Action and Awareness Fund (ECCC-CAAF)","doi-asserted-by":"publisher","award":["05265-2019"],"award-info":[{"award-number":["05265-2019"]}],"id":[{"id":"10.13039\/501100008638","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008638","name":"Environment and Climate Change Canada Climate Action and Awareness Fund (ECCC-CAAF)","doi-asserted-by":"publisher","award":["EDF-CA-2021i023"],"award-info":[{"award-number":["EDF-CA-2021i023"]}],"id":[{"id":"10.13039\/501100008638","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake\u2019s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p &lt; 0.05), compared to using matchups from the entire lake (R2 = 0.38, p &lt; 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.<\/jats:p>","DOI":"10.3390\/rs16193553","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"3553","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes"],"prefix":"10.3390","volume":"16","author":[{"given":"Forough","family":"Fendereski","sequence":"first","affiliation":[{"name":"School of Environment and Sustainability, University of Saskatchewan, 117 Science Place, Saskatoon, SK S7N 5C8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8199-1472","authenticated-orcid":false,"given":"Irena F.","family":"Creed","sequence":"additional","affiliation":[{"name":"Department of Physical and Environmental Sciences, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada"}]},{"given":"Charles G.","family":"Trick","sequence":"additional","affiliation":[{"name":"Department of Health and Society, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1038\/s41579-018-0040-1","article-title":"Cyanobacterial blooms","volume":"16","author":"Huisman","year":"2018","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1038\/s41586-019-1648-7","article-title":"Widespread global increase in intense lake phytoplankton blooms since the 1980s","volume":"574","author":"Ho","year":"2019","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1038\/s41561-021-00887-x","article-title":"Global mapping reveals increase in lacustrine algal blooms over the past decade","volume":"15","author":"Hou","year":"2022","journal-title":"Nat. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4009","DOI":"10.1016\/j.rse.2008.06.002","article-title":"Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll-a and phycocyanin","volume":"112","author":"Randolph","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Reid, J.L., Bergman, J.N., Kadykalo, A.N., Taylor, J.J., Twardek, W., Rytwinski, T., Chhor, A.D., Frempong-Manso, A., Martel, A.L., and Lapointe, N.W.R. (2022). Developing a national level evidence-based toolbox for addressing freshwater biodiversity threats. Biol. Conserv., 269.","DOI":"10.1016\/j.biocon.2022.109533"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11270-009-0053-5","article-title":"Control of algal scum using top-down biomanipulation approaches and ecosystem health assessments for efficient reservoir management","volume":"205","author":"An","year":"2010","journal-title":"Water. Air. Soil. Pollut."},{"key":"ref_7","first-page":"2","article-title":"Freshwater harmful algal blooms: Toxins and children\u2019s health","volume":"44","author":"Weirich","year":"2014","journal-title":"Curr. Probl. Pediatr. Adolesc. Health Care"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Guo, Q., Wu, X., Bing, Q., Pan, Y., Wang, Z., Fu, Y., Wang, D., and Liu, J. (2016). Study on retrieval of chlorophyll-a concentration based on Landsat OLI Imagery in the Haihe River, China. Sustainability, 8.","DOI":"10.3390\/su8080758"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109041","DOI":"10.1016\/j.ecolind.2022.109041","article-title":"Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine","volume":"140","author":"Kislik","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.rse.2017.04.017","article-title":"Ocean-colour products for climate-change studies: What are their ideal characteristics?","volume":"203","author":"Sathyendranath","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1590\/0001-3765201720170125","article-title":"Remote sensing of the chlorophyll-a based on OLI\/Landsat-8 and MSI\/Sentinel-2A (Barra Bonita reservoir, Brazil)","volume":"90","author":"Watanabe","year":"2017","journal-title":"An. Acad. Bras. Cienc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.3390\/rs9121265","article-title":"A 30-year assessment of phytoplankton blooms in Erhai Lake using Landsat imagery: 1987 to 2016","volume":"9","author":"Tan","year":"2017","journal-title":"Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.rse.2016.12.013","article-title":"Using Landsat to extend the historical record of lacustrine phytoplankton blooms: A Lake Erie case study","volume":"191","author":"Ho","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.jglr.2019.03.011","article-title":"Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: Implications for water quality remote sensing","volume":"45","author":"Sayers","year":"2019","journal-title":"J. Great Lakes Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dallosch, M.A., and Creed, I.F. (2021). Optimization of Landsat chl-a retrieval algorithms in Freshwater Lakes through classification of optical water types. J. Remote Sens., 13.","DOI":"10.3390\/rs13224607"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.rse.2019.04.027","article-title":"A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types","volume":"229","author":"Neil","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"169152","DOI":"10.1016\/j.scitotenv.2023.169152","article-title":"Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine","volume":"912","author":"Zhao","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_22","unstructured":"Environment Canada Manitoba Water Stewardship (2011). State of Lake Winnipeg: 1999\u20132007, Manitoba Water Stewardship and Environment Canada."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.scitotenv.2015.09.106","article-title":"Lake Winnipeg Basin: Advocacy, challenges and progress for sustainable phosphorus and eutrophication control","volume":"542","author":"Ulrich","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_24","unstructured":"Zar, J.H. (1999). Biostatistical Analysis, Prentice Hall. [4th ed.]."},{"key":"ref_25","unstructured":"Wheater, C.P., and Cook, P.A. (2005). Using Statistics to Understand the Environment, The Taylor & Francis e-Library."},{"key":"ref_26","unstructured":"APHA (American Public Health Association), AWWA (American Water Works Association), and WPCF (Water Pollution Control Federation) (1998). Standard Methods for the Examination of Water and Wastewater, APHA. [20th ed.]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13237","DOI":"10.1029\/95JC00458","article-title":"The lognormal distribution as a model for bio-optical variability in the sea","volume":"100","author":"Campbell","year":"1995","journal-title":"J. Geophys. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2009.10.004","article-title":"Atmospheric correction of ENVISAT\/MERIS data over inland waters: Validation for European Lakes","volume":"114","author":"Guanter","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2014.04.030","article-title":"Time-series analysis of Landsat-MSS\/TM\/OLI images over Amazonian waters impacted by gold mining activities","volume":"157","author":"Lobo","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_31","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_32","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_33","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_34","unstructured":"Vermote, E., Tanr\u00e9, D., Deuz\u00e9, J.L., Herman, M., Morcrette, J.J., and Kotchenova, S.Y. (2024, June 19). Second Simulation of a Satellite Signal in the Solar Spectrum-Vector (6SV). MODIS land Surface Reflectance Science Computing Facility, User Manual Part Two. Available online: https:\/\/ltdri.org\/files\/6S\/6S_Manual_Part_1.pdf."},{"key":"ref_35","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. Tech."},{"key":"ref_36","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_37","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_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs9070644","article-title":"SNR (signal-to-noise ratio) impact on water constituent retrieval from simulated images of optically complex Amazon lakes","volume":"9","author":"Jorge","year":"2017","journal-title":"Remote Sens"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.jglr.2018.04.001","article-title":"An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg","volume":"44","author":"Binding","year":"2018","journal-title":"J. Great Lakes Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103460","DOI":"10.1016\/j.jmarsys.2020.103460","article-title":"Modelling the impact of phytoplankton cell size and abundance on inherent optical properties (IOPs) and a remotely sensed chlorophyll-a product","volume":"213","author":"Laiolo","year":"2021","journal-title":"J. Mar. Syst."},{"key":"ref_41","unstructured":"Hastie, T., Tibshirani, R., Friedman, J., and John Lu, Z.Q. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e1557","DOI":"10.1002\/ecm.1557","article-title":"Cross validation for model selection: A review with examples from ecology","volume":"93","author":"Yates","year":"2023","journal-title":"Ecol. Monogr."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ali, G., and English, C. (2019). Phytoplankton blooms in Lake Winnipeg linked to selective water-gatekeeper connectivity. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-44717-y"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10523","DOI":"10.3390\/rs70810523","article-title":"Fourteen-year record (2000\u20132013) of the spatial and temporal dynamics of floating algae blooms in Lake Chaohu, observed from time series of MODIS images","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4919","DOI":"10.1109\/JSTARS.2017.2739184","article-title":"Landsat 8\/OLI two bands ratio algorithm for chlorophyll-a concentration mapping in hypertrophic waters: An application to West Lake in Hanoi (Vietnam)","volume":"10","author":"Ha","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1006\/ecss.2001.0922","article-title":"Spatial and Temporal Distribution of Coloured Dissolved Organic Matter (CDOM) in Narragansett Bay, Rhode Island: Implications for Phytoplankton on Coastal Waters","volume":"55","author":"Keith","year":"2002","journal-title":"Estuar. Coast Shelf Sci."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.rse.2018.12.006","article-title":"Temporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite data","volume":"221","author":"Maeda","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s10661-008-0542-9","article-title":"Mapping chlorophyll-a through in situ measurements and Terra ASTER satellite data","volume":"157","author":"Nas","year":"2009","journal-title":"Environ. Monit. Assess."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1111\/gcb.16079","article-title":"Multi-decadal changes in phytoplankton biomass in northern temperate lakes as seen through the prism of landscape properties","volume":"28","author":"Paltsev","year":"2022","journal-title":"Glob. Chang. Biol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10021-021-00684-y","article-title":"Are northern lakes in relatively intact temperate forests showing signs of increasing phytoplankton biomass?","volume":"25","author":"Paltsev","year":"2022","journal-title":"Ecosystems"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.09.021","article-title":"Hunter. Remote sensing of inland waters: Challenges, progress and future directions","volume":"157","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1364\/AO.40.003575","article-title":"Optical remote sensing of chlorophyll-a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties","volume":"40","author":"Ruddick","year":"2001","journal-title":"Appl. Opt."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"17073","DOI":"10.1073\/pnas.0913800107","article-title":"Perspectives on empirical Approaches for ocean color remote sensing of chlorophyll in a changing climate","volume":"107","author":"Dierssen","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ecss.2006.09.018","article-title":"Remote sensing reflectance and inherent optical properties in the mid Chesapeake Bay","volume":"72","author":"Tzortziou","year":"2007","journal-title":"Estuar. Coast Shelf Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2012.11.011","article-title":"Climate-driven chlorophyll-a changes in a turbid estuary: Observations from satellites and implications for management","volume":"130","author":"Le","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.csr.2019.07.002","article-title":"Spatio-temporal variability of red-green chlorophyll-a index from MODIS data\u2013Case study: Chabahar Bay, SE of Iran","volume":"184","author":"Moradi","year":"2019","journal-title":"Cont. Shelf Res."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"24109","DOI":"10.1364\/OE.18.024109","article-title":"Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands","volume":"18","author":"Gilerson","year":"2010","journal-title":"Opt. Express"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3582","DOI":"10.1016\/j.rse.2008.04.015","article-title":"A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation","volume":"112","author":"Gitelson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zeng, C., and Binding, C. (2019). The effect of mineral sediments on satellite chlorophyll-a retrievals from line-height algorithms using red and near-infrared bands. Remote Sens., 11.","DOI":"10.3390\/rs11192306"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"159","DOI":"10.5589\/m08-025","article-title":"Spectral band difference effects on vegetation indices derived from multiple satellite sensor data","volume":"34","author":"Teillet","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1002\/eap.1708","article-title":"Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithm for regional freshwater monitoring","volume":"28","author":"Boucher","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.5194\/bg-8-2849-2011","article-title":"Regional scale characteristics of the seasonal cycle of chlorophyll in the Southern Ocean","volume":"8","author":"Thomalla","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"621","DOI":"10.5194\/bg-7-621-2010","article-title":"Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity","volume":"7","author":"Henson","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1016\/j.scitotenv.2017.12.001","article-title":"Genesis, goals and achievements of long-term ecological research at the global scale: A critical review of ILTER and future directions","volume":"626","author":"Mirtl","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10750-018-3758-x","article-title":"European large perialpine lakes under anthropogenic pressures and climate change: Present status, research gaps and future challenges","volume":"824","author":"Salmaso","year":"2018","journal-title":"Hydrobiologia"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2018.10.036","article-title":"Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing","volume":"221","author":"Racault","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_70","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_71","first-page":"671","article-title":"Use of Thematic Mapper data to assess water quality in Green Bay and central Lake Michigan","volume":"52","author":"Lathrop","year":"1986","journal-title":"PE&RS"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0048-9697(00)00683-5","article-title":"Mapping of the water quality of Lake Erken, Sweden, from imaging spectrometry and Landsat Thematic Mapper","volume":"268","author":"Flink","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1080\/01431160500419311","article-title":"Remote sensing of the water quality of shallow lakes: A mixture modelling approach to quantifying phytoplankton in water characterized by high-suspended sediment","volume":"27","author":"Tyler","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1080\/01431161003645840","article-title":"2011. Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand","volume":"32","author":"Allan","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-015-4585-4","article-title":"Empirical and semi-analytical chlorophyll a algorithms for multi-temporal monitoring of New Zealand lakes using Landsat","volume":"187","author":"Allan","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_76","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 selection1","volume":"42","author":"Sudheer","year":"2006","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_77","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_78","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.ecss.2008.11.013","article-title":"Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data","volume":"81","author":"Doxaran","year":"2009","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s10661-006-9535-8","article-title":"Mapping turbidity in the Charles River, Boston using a high-resolution satellite","volume":"132","author":"Hellweger","year":"2007","journal-title":"Environ. Monit. Assess."},{"key":"ref_80","unstructured":"Floricioiu, D., Rott, H., Rott, E., Dokulil, M., and Defrancesco, C. (2004, January 6\u201310). Retrieval of limnological parameters of perialpine lakes by means of MERIS data. Proceedings of the 2004 Envisat & ERS Symposium (ESA SP-572), Salzberg, Austria."},{"key":"ref_81","unstructured":"Str\u00f6mbeck, N., Candiani, G., Giardino, C., and Zilioli, E. (2003, January 10\u201313). Water quality monitoring of Lake Garda using multi-temporal MERIS data. Proceedings of the MERIS User Workshop (ESA SP-549), Frascati, Italy."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2013.03.024","article-title":"Remote sensing of chlorophyll-a as a measure of cyanobacterial biomass in Lake Bogoria, a hypertrophic, saline- alkaline, flamingo lake, using Landsat ETM","volume":"135","author":"Tebbs","year":"2013","journal-title":"Remote Sens. Env."},{"key":"ref_83","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_84","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.envpol.2007.11.003","article-title":"Use of satellite imagery to assess the trophic state of Miyun Reservoir, Beijing, China","volume":"155","author":"Zhengjun","year":"2008","journal-title":"Environ. Pollut."},{"key":"ref_85","first-page":"782506","article-title":"Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI). In Remote Sensing of the Ocean, Sea Ice, and Large Water Regions","volume":"7825","author":"Alawadi","year":"2010","journal-title":"Int. Soc. Opt. Photonics"},{"key":"ref_86","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_87","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.isprsjprs.2008.01.004","article-title":"Monitoring water quality in the coastal area of Tripoli (Lebanon) using high-resolution satellite data","volume":"63","author":"Kabbara","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3553\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:43Z","timestamp":1760112103000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3553"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":87,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193553"],"URL":"https:\/\/doi.org\/10.3390\/rs16193553","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,24]]}}}