Abstract
A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.
This is a preview of subscription content, access via your institution.
Data availability
All data used in this study are available from public domain resources.
References
Alarcon AG, German A, Aleksinko A, Ferreyra FG, Scavuzzo CM, Ferral A (2018) Spatial algal bloom characterization by Landsat 8-Oli and field data analysis. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 22–27 July 2018. DOI: https://doi.org/10.1109/IGARSS.2018.8518844
Alba G, Anabella F, Marcelo S, Andrea DGA, Ivana ET, Guillermo EI, Sandra ET, Michal FS (2020) Spectral monitoring of algal blooms in an eutrophic lake using sentinel-2. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. DOI: https://doi.org/10.1109/IGARSS.2019.8898098
Ali K, Witter D, Ortiz J (2014) Application of empirical and semi-analytical algorithms to MERIS data for estimating chlorophyll a in Case 2 waters of Lake Erie. Environ Earth Sci 71:4209–4220. https://doi.org/10.1007/s12665-013-2814-0
Ali TA, Mortula M, Atabay S (2013) Study of water quality in dubai creek using DubaiSat-1 multispectral imagery. In: Bian F, Xie Y, Cui X, Zeng Y (Eds.) Geo-Informatics in Resource Management and Sustainable Ecosystem, pp. 200–210. DOI: https://doi.org/10.1007/978-3-642-45025-9_22
Amorim CA, Moura AN (2021) Ecological impacts of freshwater algal blooms on water quality, plankton biodiversity, structure, and ecosystem functioning, Sci Total Environ 758: 143605. DOI: https://doi.org/10.1016/j.scitotenv.2020.143605
Avouris DM, Ortiz JD (2019) Validation of 2015 Lake Erie MODIS image spectral decomposition using visible derivative spectroscopy and field campaign data. J Great Lakes Res 45:466–479. https://doi.org/10.1016/j.jglr.2019.02.005
Bangyi T, Delu P, Zhihua M, Yuzhang S, Qiankun Z, Jianyu Z (2013) Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectance. Acta Oceanol Sin 32:48–56. https://doi.org/10.1007/s13131-013-0365-6
Barica J (1984) Empirical models for prediction of algal blooms and collapses, winter oxygen depletion and a freeze-out effect in lakes: summary and verification. Internationale Vereinigung Für Theoretische Und Angewandte Limnologie: Verhandlungen 22:309–319. https://doi.org/10.1080/03680770.1983.11897308
Ben-Romdhane H, Al-Musallami M, Marpu PR, Ouarda TBMJ, Ghedira H (2018) Change detection using remote sensing in a reef environment of the UAE during the extreme event of El Niño 2015–2016. Int J Remote Sens 39:6358–6382. https://doi.org/10.1080/01431161.2018.1460502
Binding CE, Stumpf RP, Shuchman RA, Sayers MJ (2020) Advances in remote sensing of great Lakes Algal Blooms. In: Crossman J, Weisener C (Eds.) Contaminants of the Great Lakes, pp. 217–232. DOI: https://doi.org/10.1007/698_2020_589
Binding CE, Zastepa A, Zeng C (2019) The impact of phytoplankton community composition on optical properties and satellite observations of the 2017 western Lake Erie algal bloom. J Great Lakes Res 45:573–586. https://doi.org/10.1016/j.jglr.2018.11.015
Bobbin J, Recknagel F (1984) Inducing explanatory rules for the prediction of algal blooms by genetic algorithms. Environ Int 27:237–242. https://doi.org/10.1016/S0160-4120(01)00095-2
Bresciani M, Adamo M, De Caolis G, Matta E, Pasquariello G, Vaiciute D, Giardino C (2014) Monitoring blooms and surface accumulation of cyanobacteria in the Curonian Lagoon by combining MERIS and ASAR data. Remote Sens Environ 146:124–135. https://doi.org/10.1016/j.rse.2013.07.040
Bresciani M, Giardino C, Bartoli M, Tavernini S, Bolpagni R, Nizzoli D (2011) Recognizing harmful algal bloom based on remote sensing reflectance band ratio. J Appl Remote Sens 5(1):053556. https://doi.org/10.1117/1.3630218
Bricaud A, Roesler C, Zaneveld JRV (1995) In situ methods for measuring the inherent optical properties of ocean waters. Limnol Oceanogr 40(2):393–410. https://doi.org/10.4319/lo.1995.40.2.0393
Brookfield AE, Hansen AT, Sullivan PL, Czuba JA, Kirk MF, Li L, Newcomer ME, Wilkinson G (2021) Predicting algal blooms: are we overlooking groundwater? Sci Total Environ 769:1444442. https://doi.org/10.1016/j.scitotenv.2020.144442
Bui MH, Pham TL, Dao TS (2017) Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network. Mar Freshw Res 68:2070–2080. https://doi.org/10.1071/MF16327
Cao H, Han L, Li L (2022) A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake China. Harmful Algae 113:102189. https://doi.org/10.1016/j.hal.2022.102189
Cao M, Qing S, Jin E, Hao Y, Zhao W (2021) A spectral index for the detection of algal blooms using Sentinel-2 Multispectral Instrument (MSI) imagery: a case study of Hulun Lake, China. Int J Remote Sens 42:4510–4531. https://doi.org/10.1080/01431161.2021.1897186
Castro CC, Gomez JAD, Martin JD, Sanchez BAH, Arango JLC, Tuya FAC, Diaz-Varela R (2021) An UAV and satellite multispectral data approach to monitor water quality in small reservoirs. Remote Sensing 12(9):1514. https://doi.org/10.3390/rs12091514
Chang KW, Shen Y, Chen PC (2004) Predicting algal bloom in the Techi reservoir using Landsat TM data. Int J Remote Sens 25:3411–3422. https://doi.org/10.1080/01431160310001620786
Chang NB, Vannah B, Yang YJ (2014) Comparative sensor fusion between hyperspectral and multispectral satellite sensors for monitoring microcystin distribution in Lake Erie. IEEE J Select Top Appl Earth Observ Remote Sens 7:2426–2442. https://doi.org/10.1109/JSTARS.2014.2329913
Chen X, Vierling L (2006) Spectral mixture analyses of hyperspectral data acquired using a tethered balloon. Remote Sens Environ 103:338–350. https://doi.org/10.1016/j.rse.2005.05.023
Cho S, Lim B, Jung J, Kim S, Chae H, Park J, Park S, Park JK (2014) Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network. Measurement 53:224–233. https://doi.org/10.1016/j.measurement.2014.03.044
Choe E, Jung KM, Yoon JS, Jang JH, Kim MJ, Lee HJ (2021) Application of spectral indices to drone-based multispectral remote sensing for algal bloom monitoring in the river. Korean J Remote Sens 37:419–430. https://doi.org/10.7780/kjrs.2021.37.3.5
Churnside J (2014) Review of profiling oceanographic lidar. Optical Eng 53:051405. https://doi.org/10.1117/1.OE.53.5.051405
Clark JM, Schaeffer BA, Darling JA, Urquhart EA, Johnston JM, Ignatius AR, Myer MH, Loftin KA, Werdell PJ, Stumpf RP (2021) Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking water sources. Ecol Ind 80:84–95. https://doi.org/10.1016/j.ecolind.2017.04.046
Coad P, Cathers B, Ball JE, Kadluczka R (2014) Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network. Environ Model Softw 61:393–409. https://doi.org/10.1016/j.envsoft.2014.07.011
Cruz RC, Costa PR, Vinga S, Kripphal L, Lopes MB (2021) A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination. J Marine Sci Eng 9:283. https://doi.org/10.3390/jmse9030283
De Santi F, Luciani G, Bresciani M, Giardino C, Lovergine FP, Pasquariello G, Vaiciute D, De Carolis G (2019) Synergistic use of synthetic aperture radar and optical imagery to monitor surface accumulation of cyanobacteria in the Curonian Lagoon. J Marine Sci Eng 7:461. https://doi.org/10.3390/jmse7120461
Dev PJ, Sukenik A, Mishra DR, Ostrovsky I (2022) Cyanobacterial pigment concentrations in inland waters: novel semi-analytical algorithms for multi- and hyperspectral remote sensing data. Sci Total Environ 805:150423. https://doi.org/10.1016/j.scitotenv.2021.150423
Duquesne F, Vallaeys V, VidaurrePJb, Hanert E, (2021) A coupled ecohydrodynamic model to predict algal blooms in Lake Titicaca. Ecol Modell 440:109418. https://doi.org/10.1016/j.ecolmodel.2020.109418
Free G, Bresciani M, Pinardi M, Giardino C, Alikas K, Kangro K, Rõõm E-I, Vaičiūtė D, Bučas M, Tiškus E, Hommersom A, Laanen M, Peters S (2021) Detecting climate driven changes in chlorophyll-a using high frequency monitoring: the impact of the 2019 European Heatwave in Three Contrasting Aquatic Systems. Sensors 21(18):6242. https://doi.org/10.3390/s21186242
Free G, Bresciani M, Pinardi M, Peters S, Laanen M, Padula R, Cingolani A, Charavgis F, Giardano C (2022) Shorter blooms expected with longer warm periods under climate change: an example from a shallow meso-eutrophic Mediterranean lake. Hydrobiologia 849:3963–3978. https://doi.org/10.1007/s10750-021-04773-w
Gilerson AA, Gitlson AA, Zhou J, Gurlin D, Moses W, Ioannou I, Ahmed SA (2010) Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Opt Express 18:24109–24125. https://doi.org/10.1364/OE.18.024109
Glaser GH, Saliba MS (1972) Application of sparse matrices to analytical photogrammetry. In: Rose DJ, Willoughby RA (Eds.) Sparse matrices and their applications. The IBM Research Symposia Series. Springer, Boston, MA. DOI: https://doi.org/10.1007/978-1-4615-8675-3_12
Gobler CJ (2020) Climate change and harmful algal blooms: insights and perspective. Harmful Algae 91:101731. https://doi.org/10.1016/j.hal.2019.101731
Gray A, Krolikowski M, Fretwell P, Convey P, Peck LS, Mendelova M, Smith AG, Davey MP (2021) Remote sensing phenology of antarctic green and red snow algae using worldview satellites. Front. Plant Sci. 12:671981. https://doi.org/10.3389/fpls.2021.671981
Griffith AW, Gobler CJ (2020) Harmful algal blooms: a climate change co-stressor in marine and freshwater ecosystems. Harmful Algae 91:101590. https://doi.org/10.1016/j.hal.2019.03.008
Grishin MY, Lednev VN, Pershin SM, Bunkin AF, Kobylyanskiy VV, Ermakov SA, Kapustin IA, Molkov AA (2016) Laser remote sensing of an algal bloom in a freshwater reservoir. Laser Physics 26:125601. https://doi.org/10.1088/1054-660X/26/12/125601
Hamilton G, Mcvinish R, Mengersen K (2009) Bayesian model averaging for harmful algal bloom prediction. Ecol Appl 19:1805–1814. https://doi.org/10.1890/08-1843.1
Han C, Guo J, Wen L, Li S, Tian Y (2011) Prospect of monitoring and management of water blooms for airborne LIDAR. Procedia Environ Sci 10:2466–2471. https://doi.org/10.1016/j.proenv.2011.09.384
Hang X, Li Y, Li X, Xu M (2022) Sun L (2022) Estimation of chlorophyll-a concentration in Lake Taihu from gaofen-1 wide-field-of-view data through a machine learning trained algorithm. J Meteorol Res 36:208–226. https://doi.org/10.1007/s13351-022-1146-y
Hatfield RG, Bean T, Turner AD, Lees DN, Lowther J, Lewis A, Baker-Austin C (2019) Development of a TaqMan qPCR assay for detection of Alexandrium spp and application to harmful algal bloom monitoring Toxicon: X 2: 100011. DOI: https://doi.org/10.1016/j.toxcx.2019.100011
He S, Ma X, Wu Y (2018) Long time sequence monitoring of Chaohu algal blooms based on multi-source optical and radar remote sensing. Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), 8–20 June 2018. DOI: https://doi.org/10.1109/EORSA.2018.8598609
Hill PR, Kumar A, Temimi M, Bull DR (2020) HABNet: machine learning, remote sensing-based detection of harmful algal blooms. IEEE J Select Top Appl Earth Observ Remote Sens 13:3229–3239. https://doi.org/10.1109/JSTARS.2020.3001445
Hoogenboom HJ, Dekker AG, Althuis IA (1998) Simulation of AVIRIS sensitivity for detecting chlorophyll over coastal and inland waters. Remote Sens Environ 65:333–340. https://doi.org/10.1016/S0034-4257(98)00042-X
Hou X, Feng L, Dai Y, Hu C, Gibson L, Tang J, Lee Z, Wang Y, Cai X, Liu J, Zheng Y, Zheng C (2022) Global mapping reveals increase in lacustrine algal blooms over the past decade. Nat Geosci 15:130–134. https://doi.org/10.1038/s41561-021-00887-x
Johansen RA, Beck R, Stumpf LJ, Tokars R, Tolbert C, McGhan C, Black T, Ma O, Xu M, Liu H, Reif M, Emery E (2019) HABSat-1: assessing the feasibility of using CubeSats for the detection of cyanobacterial harmful algal blooms in inland lakes and reservoirs. Lake Reservoir Manage 35:193–207. https://doi.org/10.1080/10402381.2019.1609146
Khan RM, Salehi B, Mahdianpari M, Mohammadimanesh F, Mountrakis G, Quackenbush LJ (2021) A meta-analysis on harmful algal bloom (HAB) detection and monitoring: a remote sensing perspective. Remote Sens 13(21):4347. https://doi.org/10.3390/rs13214347
Kislik C, Dronova I, Kelly M (2018) UAVs in support of algal bloom research: a review of current applications and future opportunities. Drones 2:35. https://doi.org/10.3390/drones2040035
Klemas V (2012) Remote sensing of algal blooms: an overview with case studies. J Coastal Res 28:34–43. https://doi.org/10.2112/JCOASTRES-D-11-00051.1
Klemas VV (2015) Coastal and environmental remote sensing from unmanned aerial vehicles: an overview. J Coastal Res 31:1260–1267. https://doi.org/10.2112/JCOASTRES-D-15-00005.1
Kudela RM, Palacios SL, Austerberry DC, Accorsi EK, Guild LS, Torres-Perez J (2015) Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sens Environ 167:196–205. https://doi.org/10.1016/j.rse.2015.01.025
Kutser T (2009) Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters. Int J Remote Sens 30:4401–4425. https://doi.org/10.1080/01431160802562305
Kwon YS, Pyo JC, Kwon YH, Duan H, Cho KH, Park Y (2020) Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir. Remote Sens Environ 236:111517. https://doi.org/10.1016/j.rse.2019.111517
Lee ZP, Carder KL, Arnone RA (2002) Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Appl Opt 41:5755–5772. https://doi.org/10.1364/AO.41.005755
Legleiter CJ, King TV, Carpenter KD, Hall NC, Mumford AC, Slonecker T, Graham JL, Stengel VG, Simon N, Rosen BH (2022) Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): a field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data. Remote Sens Environ 279:113089. https://doi.org/10.1016/j.rse.2022.113089
Lekki J, Deutsch E, Sayers M, Bosse K, Anderson R, Tokares R, Sawtell R (2019a) Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes. J Great Lakes Res 45:434–443. https://doi.org/10.1016/j.jglr.2019.03.014
Lekki J, Ruberg S, Binding C, Anderson R, Woude AV (2019b) Airborne hyperspectral and satellite imaging of harmful algal blooms in the Great Lakes Region: successes in sensing algal blooms. J Great Lakes Res 45:405–412. https://doi.org/10.1016/j.jglr.2019.03.016
Li Y, Zhang Y, Shi K, Zhou Y, Zhang Y, Liu X, Guo Y (2018) Spatiotemporal dynamics of chlorophyll-a in a large reservoir as derived from Landsat 8 OLI data: understanding its driving and restrictive factors. Environ Sci Pollut Res 25:1359–1374. https://doi.org/10.1007/s11356-017-0536-7
Lobo FL, Nagel GW, Maciel DA, Carvalho LAS, Martins VS, Barbosa CCF, Novo EMLM (2021) AlgaeMAp: algae bloom monitoring application for inland waters in Latin America. Remote Sensing 13:2874. https://doi.org/10.3390/rs13152874
Lyu P, Malang Y, Liu HHT, Lai J, Liu J, Jiang B, Qu M, Anderson S, Lefebvre DD, Wang Y (2017) Autonomous cyanobacterial harmful algal blooms monitoring using multirotor UAS. Int J Remote Sens 38:2818–2843. https://doi.org/10.1080/01431161.2016.1275058
Mishra S, Mishra DR (2012) Normalized difference chlorophyll index: a novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sens Environ 117:394–406. https://doi.org/10.1016/j.rse.2011.10.016
Mishra S, Stumpf RP, Meredith A (2019) Evaluation of RapidEye data for mapping algal blooms in inland waters. Int J Remote Sens 40:2811–2819. https://doi.org/10.1080/01431161.2018.1533657
Molkov AA, Dolin LS, Pelevin VV, Kapustin IA, Belyaev NA, Konovalov BV, Kremenetskiy VV (2018) Lidar measurements spatial variability of optical properties of water in eutrophic reservoirs. Proc. SPIE 10784, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions, 107841A. DOI: https://doi.org/10.1117/12.2500483
Moore TS, Churnside JH, Sullivan JM, Twardowski MS, Nayak AR, McFarland MN, Stockley ND, Gould RW, Johengen TH, Ruberg SA (2019) Vertical distributions of blooming cyanobacteria populations in a freshwater lake from LIDAR observations. Remote Sens Environ 225:347–367. https://doi.org/10.1016/j.rse.2019.02.025
Moreno-Ostos E, Cruz-Pizarro L, Basanta A (2009) The influence of wind-induced mixing on the vertical distribution of buoyant and sinking phytoplankton species. Aquat Ecol 43:271–284. https://doi.org/10.1007/s10452-008-9167-x
Mueller JL (1979) Prospects for measuring phytoplankton bloom extent and patchiness using remotely sensed ocean color images: an example. In: Taylor DL, Seliger HH (Eds.) Toxic Dinoflagellate Blooms. Elsevier, New York, pp. 303–308
Niculescu S, Boissonnat J-B, Lardeux C, Roberts D, Hanganu J, Billey A, Constantinescu A, Doroftei M (2020) Synergy of high-resolution radar and optical images satellite for identification and mapping of wetland macrophytes on the danube delta. Remote Sensing 12(14):2188. https://doi.org/10.3390/rs12142188
Niroumond-Jadidi M, Bovolo F (2021) Water quality retrieval and algal bloom detection using high-resolution Cubesat imagery. ISPRS Ann. Photogramm. Remote Sens Spatial Inf Sci 3:191–195. https://doi.org/10.5194/isprs-annals-V-3-2021-191-2021
Odermatt D, Gitelson A, Brando VE, Schaepman M (2012) Review of constituent retrieval in optically deep and complex waters from satellite imagery. Remote Sens Environ 118:116–126. https://doi.org/10.1016/j.rse.2011.11.013
Ogashawara I (2019) The use of sentinel-3 imagery to monitor cyanobacterial blooms. Environments 6:60. https://doi.org/10.3390/environments6060060
Ortiz DA, Wilkinson GM (2021) Capturing the spatial variability of algal bloom development in a shallow temperate lake. Freshw Biol 66:2064–2075. https://doi.org/10.1111/fwb.13814
O’Shea RE, Pahlevan N, Smith B, Bresciani M, Egerton T, Giardano C, Li L, Moore T, Ruiz-Verdu A, Ruberg S, Simis SGH, Stumpf R, Vaiciute D (2021) Advancing cyanobacteria biomass estimation from hyperspectral observations: demonstrations with HICO and PRISMA imagery. Remote Sens Environ 266:112693. https://doi.org/10.1016/j.rse.2021.112693
Paerl HW, Fulton RS, Moisander PH, Dyble J (2001) Harmful freshwater algal blooms, with an emphasis on Cyanobacteria. The Scientific World 1:76–11. https://doi.org/10.1100/tsw.2001.16
Pahlevan N, Smith B, Alikas K, Anstee J, Barbosa C (2022) Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sens Environ 270:112860. https://doi.org/10.1016/j.rse.2021.112860
Palmer SCJ, Pelevin VV, Goncharenko I, Kovacs AW, Zlinsky A, Presing M, Horvath H, Nicolas-Perea V, Baltzer H, Toth VR (2013) Ultraviolet fluorescence LiDAR (UFL) as a measurement tool for water quality parameters in Turbid Lake conditions. Remote Sensing 5:4405–4422. https://doi.org/10.3390/rs5094405
Pettersson LH, Pozdnyakov D (2013) Qualification, species variety, and consequences of harmful algal blooms (HABs). In: monitoring of harmful algal blooms. Springer Praxis Books. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-540-68209-7_1
Paul VJ (2008) Global warming and cyanobacterial harmful algal blooms. In: Hudnell HK (Ed.) cyanobacterial harmful algal blooms: state of the science and research needs. Advances in Experimental Medicine and Biology, vol 619, pp. 239–257. Springer, New York, NY. DOI: https://doi.org/10.1007/978-0-387-75865-7_11
Pölönen I, Puupponen HH, Honkavaara E, Lindfors A, Saari H, Markelin L, Hakala T, Nurminen K (2014) UAV-based hyperspectral monitoring of small freshwater area. Proceedings volume 9239, remote sensing for agriculture, ecosystems, and hydrology XVI; 923912 (2014) DOI: https://doi.org/10.1117/12.2067422
Rotta L, Alcantara E, Park E, Bernardo N, Watanabe F (2021) A single semi-analytical algorithm to retrieve chlorophyll-a concentration in oligo-to-hypereutrophic waters of a tropical reservoir cascade. Ecol Indicators 120:106913. https://doi.org/10.1016/j.ecolind.2020.106913
Sagan V, Peterson KT, Maimaitijiang M, Sidike P, Sloan J, Greeling BA, Maalouf S, Adams C (2020) Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning and cloud computing. Earth-Sci Rev 205:103187. https://doi.org/10.1016/j.earscirev.2020.103187
Sayers M, Fahnenstiel GL, Shuchman RA, Whitley M (2016) Cyanobacteria blooms in three eutrophic basins of the Great Lakes: a comparative analysis using satellite remote sensing. Int J Remote Sens 37(17):4148–4171. https://doi.org/10.1080/01431161.2016.1207265
Sayers MJ, Bosse KR, Shuchman RA, Ruberg SA, Fahnenstiel GL, Leshkevich GA, Stuart DG, Johengen TH, Burtner AM, Palladino D (2019a) Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: implications for water quality remote sensing. J Great Lakes Res 45(3):490–507. https://doi.org/10.1016/j.jglr.2019.03.011
Sayers MJ, Grimm AG, Shuchman RA, Bosse KR, Fahnenstiel GL, Ruberg SA, Leshkevich GA (2019b) Satellite monitoring of harmful algal blooms in the Western Basin of Lake Erie: a 20-year time-series. J Great Lakes Res 45:508–521. https://doi.org/10.1016/j.jglr.2019.01.005
Shang S, Lee Z, Lin G, Hu C, Shi L, Zhang Y, Li X, Wu J, Yan J (2017) Sensing an intense phytoplankton bloom in the western Taiwan Strait from radiometric measurements on a UAV. Remote Sens Environ 198:85–94. https://doi.org/10.1016/j.rse.2017.05.036
Shen L, Xu H, Guo X (2012) Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework. Sensors 12:7778–7803. https://doi.org/10.3390/s120607778
Smith B, Pahlevan N, Schalles J, Ruberg S, Errera R, Ma R, Giardano C, Bresciani M, Barbosa C, Moore T, Fernandez V, Alikas K, Kangro K (2021) A chlorophyll-a algorithm for landsat-8 based on mixture density networks. Front Remote Senshttps://doi.org/10.3389/frsen.2020.62367
Strong AE (1974) Remote sensing of algal blooms by aircraft and satellite in Lake Erie and Utah Lake. Remote Sens Environ 3:99–107. https://doi.org/10.1016/0034-4257(74)90052-2
Stumpf RP (2001) Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms. Hum Ecol Risk Assess 7:1363–1368. https://doi.org/10.1080/20018091095050
Stumpf RP, Culver ME, Tester PA, Tomlinson M, Kirkpatrick GJ, Pederson BA, Truby E, Ransibrahmanakul V, Soracco M (2003) Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data. Harmful Algae 2:147–160. https://doi.org/10.1016/S1568-9883(02)00083-5
Stumpf RP, Davis TW, Wynne TT, Graham JL, Loftin KA, Johengen TH, Gossiaux D, Palladino D, Burtner A (2016) Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Harmful Algae 54:160–173. https://doi.org/10.1016/j.hal.2016.01.005
Stumpf RP, Tomlinson MC (2005) Remote sensing of harmful algal blooms. In: Miller, R.; del Castillo C, McKee B (Eds.) Remote sensing of coastal aquatic environments: technologies, techniques and applications. Dordrecht, the Netherlands: Kluwer Academic Publishers, pp. 277–292
Sun X, Zhang Y, Shi K, Zhang Y, Li N, Wang W, Huang X, Qin B (2022) Monitoring water quality using proximal remote sensing technology. Sci Total Environ 803:149805. https://doi.org/10.1016/j.scitotenv.2021.149805
Topp SN, Pavelsky TM, Jensen D, Simard M, Ross MRV (2020) Research trends in the use of remote sensing for inland water quality science: moving towards multidisciplinary applications. Water 12(1):169. https://doi.org/10.3390/w12010169
Toth C, Jozkow G (2016) Remote sensing platforms and sensors: a survey. ISPRS J Photogramm Remote Sens 115:22–36. https://doi.org/10.1016/j.isprsjprs.2015.10.004
Trainer VL, Moore SK, Hallegraeff G, Kudela RM, Clement A, Mordones JI, Cochlan WP (2020) Pelagic harmful algal blooms and climate change: lessons from nature’s experiments with extremes. Harmful Algae 91:101591. https://doi.org/10.1016/j.hal.2019.03.009
Veettil BK, Bianchini N (2014) A remote sensing approach for monitoring seasonal variations in the water quality of Lake Guaiba, Southern Brazil. Recent Trends Civil Eng Technol 4:1–10
Veettil BK, Quang NX (2018) Environmental changes near the Mekong Delta in Vietnam using remote sensing. Rendiconti Lincei Scienze Fisiche e Naturali 29:639–647. https://doi.org/10.1007/s12210-018-0695-6
Viero AP (2022) Estudos complementares da avaliação da capacidade de recepção de efluentes domésticos tratados pela ete de Osório na Lagoa dos Barros e avaliação de alternativas de reúso. Project report submitted to CORSAN, Rio Grande do Sul, Brazil, pp. 90
Wang G, Li J, Zhang B, Shen Q, Zhang F (2015) Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images. Chin J Oceanol Limnol 33(1):139–148. https://doi.org/10.1007/s00343-015-4019-8
Wang G, Li J, Zhang B, Cai Z, Zhang F, Shen Q (2017) Synthetic aperture radar detection and characteristic analysis of cyanobacterial scum in Lake Taihu. J. Appl. Remote Sens. 11(1):012006. https://doi.org/10.1117/1.JRS.11.012006
Wang W, Shi K, Zhang Y, Li N, Sun X, Zhang D, Zhang Y, Qin B, Zhu G (2022) A ground-based remote sensing system for high-frequency and real-time monitoring of phytoplankton blooms. J Hazard Mater 439:129623. https://doi.org/10.1016/j.jhazmat.2022.129623
Watanabe F, Alcântara E, Imai N, Rodrigues T, Bernardo N (2018) Estimation of chlorophyll-a concentration from optimizing a semi-analytical algorithm in productive inland waters. Remote Sensing 10(2):227. https://doi.org/10.3390/rs10020227
Wei B, Sugiura N, Maekawa T (2001) Use of artificial neural network in the prediction of algal blooms. Water Res 35:2022–2028. https://doi.org/10.1016/S0043-1354(00)00464-4
Weirich CA, Miller TR (2014) Freshwater harmful algal blooms: toxins and children’s health. Curr Probl Pediatr Adolesc Health Care 44:2–24. https://doi.org/10.1016/j.cppeds.2013.10.007
Xia R, Wang G, Zhang Y, Yang P, Yang Z, Ding S, Jia X, Yang C, Liu C, Ma S, Lin J, Wang X, Hou X, Zhang K, Gao X, Duan P, Qian C (2020) River algal blooms are well predicted by antecedent environmental conditions. Water Research 185:116221. https://doi.org/10.1016/j.watres.2020.116221
Xu D, Pu Y, Zhu M, Luan Z, Shi K (2021) Automatic detection of algal blooms using sentinel-2 MSI and Landsat OLI images. IEEE J Select Top Appl Earth Observ Remote Sens 14:8497–8511. https://doi.org/10.1109/JSTARS.2021.3105746
Zeng C, Binding C (2019) The effect of mineral sediments on satellite chlorophyll-a retrievals from line-height algorithms using red and near-infrared bands. Remote Sensing 11:2306. https://doi.org/10.3390/rs11192306
Zhang C, Zhang J (2015) Current techniques for detecting and monitoring algal toxins and causative harmful algal blooms. J Environ Anal Chem 2:1. https://doi.org/10.4172/2380-2391.1000123
Zheng G, DiGiacomo PM (2017) Detecting phytoplankton diatom fraction based on the spectral shape of satellite-derived algal light absorption coefficient. Limnol Oceanogr 63:S85–S98. https://doi.org/10.1002/lno.10725
Funding
This study was supported by Companhia Riograndense de Saneamento – CORSAN, Porto Alegre, Brazil, and the Institute of Geosciences, Universidade Federal do Rio Grande do Sul -UFRGS, Porto Alegre, Brazil.
Author information
Authors and Affiliations
Contributions
BKV designed the study. BKV, SBAR, AV, ABK, and CG contributed equally to the manuscript.
Corresponding author
Ethics declarations
Ethical approval
Hereby, we consciously assure that for the manuscript/insert title/the following is fulfilled: (1) this material is the authors’ own original work, which has not been previously published elsewhere. (2) The paper is not currently being considered for publication elsewhere. (3) The paper reflects the authors’ own research and analysis in a truthful and complete manner. (4) The paper properly credits the meaningful contributions of co-authors and co-researchers. (5) The results are appropriately placed in the context of prior and existing research. (6) All sources used are properly disclosed (correct citation). Literally copying of text must be indicated as such by using quotation marks and giving proper references. (7) All authors have been personally and actively involved in substantial work leading to the paper and will take public responsibility for its content.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Garrigues
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Rolim, S.B.A., Veettil, B.K., Vieiro, A.P. et al. Remote sensing for mapping algal blooms in freshwater lakes: a review. Environ Sci Pollut Res 30, 19602–19616 (2023). https://doi.org/10.1007/s11356-023-25230-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-25230-2
Keywords
- Algal blooms
- Electromagnetic spectrum
- Remote sensing
- Phytoplankton
- Spatiotemporal bloom mapping