{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T01:08:07Z","timestamp":1768007287036,"version":"3.49.0"},"reference-count":81,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"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":["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","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\u2014Climate Action and Awareness Fund","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\u2014Climate Action and Awareness Fund","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>This study investigates the escalating impact of wildfire smoke on the remote sensing of phytoplankton biomass in freshwater systems. Wildfire smoke disrupts the accuracy of Chlorophyll-a (Chl-a) retrieval models, with Chl-a often used as a proxy for quantifying phytoplankton biomass. Given the increasing frequency and intensity of wildfires, there is a need for the development and refinement of remote sensing methodologies to effectively monitor phytoplankton dynamics under wildfire-impacted conditions. Here we developed a novel approach using Landsat\u2019s coastal\/aerosol band (B1) to screen for and categorize levels of wildfire smoke interference. By excluding high-interference data (B1 reflectance &gt; 0.07) from the calibration set, Chl-a retrieval model performance using different Landsat band formulas improved significantly, with R2 increasing from 0.55 to as high as 0.80. Our findings demonstrate that Rayleigh-corrected reflectance, combined with B1 screening, provides a robust method for monitoring phytoplankton biomass even under moderate smoke interference, outperforming full atmospheric correction methods. This approach enhances the reliability of remote sensing in the face of increasing wildfire events, offering a valuable tool for the effective management of aquatic environments.<\/jats:p>","DOI":"10.3390\/rs16193605","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T06:10:27Z","timestamp":1727417427000},"page":"3605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Tracking Phytoplankton Biomass Amid Wildfire Smoke Interference Using Landsat 8 OLI"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2246-3022","authenticated-orcid":false,"given":"Sassan","family":"Mohammady","sequence":"first","affiliation":[{"name":"School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK S7N 5C8, Canada"}]},{"given":"Kevin J.","family":"Erratt","sequence":"additional","affiliation":[{"name":"Department of Physical and Environmental Sciences, University of Toronto, Toronto, ON M1C 1A4, 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, Toronto, ON M1C 1A4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"215","DOI":"10.5268\/IW-4.2.753","article-title":"Limnology and oceanography: Two estranged twins reuniting by global change","volume":"4","author":"Downing","year":"2014","journal-title":"Inland. Waters"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5240","DOI":"10.1111\/gcb.16838","article-title":"Climate change amplifies the risk of potentially toxigenic cyanobacteria","volume":"29","author":"Erratt","year":"2023","journal-title":"Glob. Change Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e1373","DOI":"10.1002\/wat2.1373","article-title":"Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum","volume":"6","author":"Wurtsbaugh","year":"2019","journal-title":"Wiley Interdiscip. Rev.-Water"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"102264","DOI":"10.1016\/j.hal.2022.102264","article-title":"Harmonizing science and management options to reduce risks of cyanobacteria","volume":"116","author":"Erratt","year":"2022","journal-title":"Harmful Algae"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1016\/j.scitotenv.2017.08.219","article-title":"Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake","volume":"612","author":"Klinger","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"134305","DOI":"10.1016\/j.scitotenv.2019.134305","article-title":"Remote sensing of cyanobacterial blooms in a hypertrophic lagoon (Albufera of Val\u00e8ncia, Eastern Iberian Peninsula) using multitemporal Sentinel-2 images","volume":"698","author":"Vicente","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mishra, S., Stumpf, R.P., Schaeffer, B.A., Werdell, P.J., Loftin, K.A., and Meredith, A. (2019). Measurement of cyanobacterial bloom magnitude using satellite remote sensing. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-54453-y"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.ecolind.2008.11.013","article-title":"Phytoplankton bloom status: Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA","volume":"9","author":"Boyer","year":"2009","journal-title":"Ecol. Indic."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Younos, T., and Parece, T. (2015). Remote sensing for regional lake water quality assessment: Capabilities and limitations of current and upcoming satellite systems. Advances in Watershed Science and Assessment, Springer International Publishing. The Handbook of Environmental Chemistry 33.","DOI":"10.1007\/978-3-319-14212-8"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1007\/s10661-020-08631-5","article-title":"Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs","volume":"192","author":"Papenfus","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_12","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_13","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. Gt. Lakes Res."},{"key":"ref_14","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_15","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_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.09.021","article-title":"Remote sensing of inland waters: Challenges, progress, and future directions","volume":"157","author":"Palmer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","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. Change Biol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tan, W., Liu, P., Liu, Y., Yang, S., and Feng, S. (2017). A 30-year assessment of phytoplankton blooms in Erhai lake using Landsat imagery: 1987 to 2016. Remote Sens., 9.","DOI":"10.3390\/rs9121265"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1080\/01431161.2017.1407048","article-title":"Haze removal for new generation optical sensors","volume":"39","author":"Hong","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pinardi, M., Stroppiana, D., Caroni, R., Parigi, L., Tellina, G., Free, G., Giardino, C., Albergel, C., and Bresciani, M. (2023). Assessing the impact of wild fires on water quality using satellite remote sensing: The Lake Baikal case study. Front. Remote Sens., 4.","DOI":"10.3389\/frsen.2023.1107275"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"120713","DOI":"10.1016\/j.envpol.2022.120713","article-title":"Wildfire impacts on surface water quality parameters: Cause of data variability and reporting needs","volume":"317","author":"Raoelison","year":"2023","journal-title":"Environ. Pollut."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Murphy, S.F., Alpers, C.N., Anderson, C.W., Banta, J.R., Blake, J.M., Carpenter, K.D., Clark, G.D., Clow, D.W., Hempel, L.A., and Martin, D.A. (2023). A call for strategic water-quality monitoring to advance assessment and prediction of wildfire impacts on water supplies. Front. Water., 5.","DOI":"10.3389\/frwa.2023.1144225"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e2021WR030699","DOI":"10.1029\/2021WR030699","article-title":"Wildfire induces changes in receiving waters: A review with considerations for water quality management","volume":"58","author":"Paul","year":"2022","journal-title":"Water Resour. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Robinne, F., Miller, C., Parisien, M., Emelko, M.B., Bladon, K.D., Silins, U., and Flannigan, M. (2016). A global index for mapping the exposure of water resources to wildfire. Forests, 7.","DOI":"10.3390\/f7010022"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2114069119","DOI":"10.1073\/pnas.2114069119","article-title":"Growing impact of wildfire on western US water supply","volume":"119","author":"Williams","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_28","first-page":"21","article-title":"Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape","volume":"11531","author":"Kganyago","year":"2020","journal-title":"Remote Sens. Clouds Atmos. XXV"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pinto, C.T., Jing, X., and Leigh, L. (2020). Evaluation analysis of Landsat Level-1 and Level-2 data products using in situ measurements. Remote Sens., 12.","DOI":"10.3390\/rs12162597"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1002\/lol2.10164","article-title":"Effects of lake warming on the seasonal risk of toxic cyanobacteria exposure","volume":"5","author":"Hayes","year":"2020","journal-title":"Limnol. Oceanogr. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1002\/lno.12352","article-title":"Both biotic and abiotic predictors explain significant variation in cyanobacteria biomass across lakes from temperate to subarctic zones","volume":"68","author":"Mackeigan","year":"2023","journal-title":"Limnol. Oceanogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1111\/fwb.13099","article-title":"Comparative effects of ammonium, nitrate and urea on growth and photosynthetic efficiency of three bloom-forming cyanobacteria","volume":"63","author":"Erratt","year":"2018","journal-title":"Freshw. Biol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0015-3796(17)30778-3","article-title":"New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton","volume":"167","author":"Jeffrey","year":"1975","journal-title":"Biochem. Physiol. Pflanz."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Velastegui-Montoya, A., Montalv\u00e1n-Burbano, N., Carri\u00f3n-Mero, P., Rivera-Torres, H., Sadeck, L., and Adami, M.G. (2023). Google Earth Engine: A global analysis and future trends. Remote Sens., 15.","DOI":"10.3390\/rs15143675"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chu, H., He, Y., Nisa, W., and Jaelani, L.M. (2021). Multi-reservoir water quality mapping from remote sensing using spatial regression. Sustainability, 13.","DOI":"10.3390\/su13116416"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jin, H., And, S.F., and Chen, C. (2023). Mapping of the spatial scope and water quality of surface water based on the Google Earth Engine cloud platform and Landsat time series. Remote Sens., 15.","DOI":"10.3390\/rs15204986"},{"key":"ref_38","first-page":"103143","article-title":"Monitoring of sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and the Google Earth Engine","volume":"66","author":"Katlane","year":"2023","journal-title":"Reg. Stud. Mar. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kwong, I.H.Y., Wong, F.K.K., and Fung, T. (2022). Automatic mapping and monitoring of marine water quality parameters in Hong Kong using Sentinel-2 image time-series and Google Earth Engine cloud computing. Front. Mar. Sci., 9.","DOI":"10.3389\/fmars.2022.871470"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10750-017-3462-2","article-title":"Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8","volume":"824","author":"Bresciani","year":"2018","journal-title":"Hydrobiologia"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e14311","DOI":"10.7717\/peerj.14311","article-title":"Comparison of iCOR and Rayleigh atmospheric correction methods on Sentinel-3 OLCI images for a shallow eutrophic reservoir","volume":"10","author":"Lefkaditis","year":"2022","journal-title":"PeerJ"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"167631","DOI":"10.1016\/j.scitotenv.2023.167631","article-title":"A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data","volume":"906","author":"Wang","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_45","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. Remote Sens., 13.","DOI":"10.3390\/rs13224607"},{"key":"ref_46","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_47","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_48","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_49","doi-asserted-by":"crossref","first-page":"4430","DOI":"10.1109\/JSTARS.2017.2723079","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. Earth Observ. Remote Sens."},{"key":"ref_50","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_51","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 Sens."},{"key":"ref_52","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_53","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_54","unstructured":"Chandrasekhar, S. (1960). Radiative Transfer, Dover Publications."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","unstructured":"Jorge, D.S.F., Barbosa, C.C.F., Carvalho, L.A.S.D., Affonso, A.G., Novo, F.D.L.L., and Lobo, F.D.L. (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_57","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_58","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8 \/ OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lu, X., Zhang, X., Li, F., Cochrane, M.A., and Ciren, P. (2021). Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions. Remote Sens., 13.","DOI":"10.3390\/rs13020196"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1080\/01431161003645840","article-title":"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_61","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_62","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_63","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10812-017-0466-7","article-title":"Monitoring of chlorophyll in water reservoirs using satellite data","volume":"84","author":"Bocharov","year":"2017","journal-title":"J. Appl. Spectrosc."},{"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 algorithms for regional freshwater monitoring","volume":"28","author":"Boucher","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_65","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":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_66","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_67","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_68","doi-asserted-by":"crossref","first-page":"5895","DOI":"10.1109\/TGRS.2013.2293662","article-title":"Haze detection and removal in remotely sensed multispectral imagery","volume":"52","author":"Makarau","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"104179","DOI":"10.1109\/ACCESS.2019.2929591","article-title":"Haze removal algorithm for optical remote sensing image based on multi-scale model and histogram characteristic","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"G04002","DOI":"10.1029\/2005JG000150","article-title":"Shrinking ponds in subarctic Alaska based on 1950\u20132002 remotely sensed images","volume":"111","author":"Riordan","year":"2006","journal-title":"J. Geophys. Res."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Guindon, B., and Zhang, Y. (2002, January 8\u201312). Robust haze reduction: An integral processing component in satellite-based land cover mapping. Proceedings of the ISPRS Commission IV Symposium, Ottawa, ON, Canada.","DOI":"10.4095\/219885"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Neagoe, I.C., Vaduva, C., and Datcu, M. (2021, January 12\u201316). Haze and smoke removal for visualization of multispectral images: A DNN physics aware architecture. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553735"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"16","DOI":"10.12962\/j20882033.v27i1.1217","article-title":"Estimation of TSS and Chl\u2014A concentration from Landsat 8\u2014OLI: The effect of atmosphere and retrieval algorithm","volume":"27","author":"Jaelani","year":"2016","journal-title":"IPTEK J. Technol. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"104304","DOI":"10.1016\/j.jconhyd.2024.104304","article-title":"Remote sensing retrieval and driving analysis of phytoplankton density in the large storage freshwater lake: A study based on random forest and","volume":"261","author":"Wang","year":"2024","journal-title":"J. Contam. Hydrol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07038992.2023.2215333","article-title":"Comparative analysis of empirical and machine learning models for Chl a extraction using Sentinel-2 and Landsat OLI Data: Opportunities, limitations, and challenges","volume":"49","author":"Chegoonian","year":"2023","journal-title":"Can. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.5194\/gmd-15-7933-2022","article-title":"Bayesian atmospheric correction over land: Sentinel-2\/MSI and Landsat 8\/OLI","volume":"15","author":"Yin","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111290","DOI":"10.1016\/j.ecolind.2023.111290","article-title":"Retrieving Lake Chl-a Concentration from Remote Sensing: Sampling Time Matters","volume":"158","author":"Yang","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Lobo, F.D.L., Nagel, G.W., Maciel, D.A., Carvalho, L.A.S.D., Martins, V.S., Barbosa, C.C.F., and Novo, E.M.L.D.M. (2021). Algae-MAp: Algae Bloom Monitoring Application for Inland Waters in Latin America. Remote Sens., 13.","DOI":"10.3390\/rs13152874"},{"key":"ref_79","unstructured":"Vanhellemont, Q., and Ruddick, K. (2016, January 9\u201313). ACOLITE for sentinel-2: Aquatic applications of MSI imagery. Proceedings of the ESA Living Planet Symposium, Prague, Czech Republic. Available online: https:\/\/odnature.naturalsciences.be\/downloads\/publications\/2016_Vanhellemont_ESALP.pdf."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.rse.2018.07.015","article-title":"Remote Sensing of Environment Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications","volume":"216","author":"Vanhellemont","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"111284","DOI":"10.1016\/j.rse.2019.111284","article-title":"Remote Sensing of Environment A harmonized image processing work flow using Sentinel-2\/MSI and Landsat-8\/LI for mapping water clarity in optically variable lake systems","volume":"231","author":"Page","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3605\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:04:52Z","timestamp":1760112292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3605"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":81,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193605"],"URL":"https:\/\/doi.org\/10.3390\/rs16193605","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]}}}