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Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

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Abstract

Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R2) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Abbas, M., Dia, S., Deutsch, E. S., & Alameddine, I. (2023). Analyzing eutrophication and harmful algal bloom dynamics in a deep Mediterranean hypereutrophic reservoir. Environmental Science and Pollution Research, 30(13), 37607–37621.

    Article  CAS  Google Scholar 

  • Abdelal, Q., Assaf, M. N., Al-Rawabdeh, A., Arabasi, S., & Rawashdeh, N. A. (2022). Assessment of Sentinel-2 and Landsat-8 OLI for small-scale inland water quality modeling and monitoring based on handheld hyperspectral ground truthing. Journal of Sensors, 2022.

  • Adusei, Y. Y., Quaye-Ballard, J., Adjaottor, A. A., & Mensah, A. A. (2021). Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 825–833.

  • Al-Fahdawi, A. A., Rabee, A. M., & Al-Hirmizy, S. M. (2015). Water quality monitoring of Al-Habbaniyah Lake using remote sensing and in situ measurements. Environmental Monitoring and Assessment, 187, 367.

    Article  Google Scholar 

  • Ansper, A., & Alikas, K. (2019). Retrieval of chlorophyll a from Sentinel-2 MSI data for the European Union water framework directive reporting purposes. Remote Sensing, 11, 64.

    Article  Google Scholar 

  • Arias-Rodriguez, L. F., Duan, Z., Sepúlveda, R., Martinez-Martinez, S. I., & Disse, M. (2020). Monitoring water quality of valle de bravo reservoir, mexico, using entire lifespan of meris data and machine learning approaches. Remote Sensing, 12, 1586.

    Article  Google Scholar 

  • Atoui, A., Hafez, H., & Slim, K. (2013). Occurrence of toxic cyanobacterial blooms for the first time in L ake K araoun, L ebanon. Water and Environment Journal, 27, 42–49.

    Article  CAS  Google Scholar 

  • Beaulieu, M., Pick, F., & Gregory-Eaves, I. (2013). Nutrients and water temperature are significant predictors of cyanobacterial biomass in a 1147 lakes data set. Limnology and Oceanography, 58(5), 1736–1746.

    Article  CAS  Google Scholar 

  • Binding, C., Greenberg, T., & Bukata, R. (2013). The MERIS maximum chlorophyll index; its merits and limitations for inland water algal bloom monitoring. Journal of Great Lakes Research, 39, 100–107.

    Article  CAS  Google Scholar 

  • Binding, C., Greenberg, T., Jerome, J., Bukata, R., & Letourneau, G. (2011). An assessment of MERIS algal products during an intense bloom in Lake of the Woods. Journal of Plankton Research, 33, 793–806.

    Article  Google Scholar 

  • Blansché, A. 2021. Package ‘fdm2id’.

    Google Scholar 

  • Boldanova, E. (2021). Modelling of transparency of Lake Baikal inferred from the Sentinel-2 data. Limnology and Freshwater Biology, 4, 1126–1129.

    Article  Google Scholar 

  • Bonansea, M., Ledesma, M., Bazán, R., Ferral, A., German, A., O'Mill, P., Rodriguez, C., & Pinotti, L. (2019). Evaluating the feasibility of using Sentinel-2 imagery for water clarity assessment in a reservoir. Journal of South American Earth Sciences, 95, 102265.

    Article  Google Scholar 

  • Bonansea, M., Rodriguez, M. C., Pinotti, L., & Ferrero, S. (2015). Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina). Remote Sensing of Environment, 158, 28–41.

    Article  Google Scholar 

  • Bramich, J., Bolch, C. J., & Fischer, A. (2021). Improved red-edge chlorophyll-a detection for Sentinel 2. Ecological Indicators, 120, 106876.

    Article  CAS  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine learning, 45, 5–32.

    Article  Google Scholar 

  • Caballero, I., Fernández, R., Escalante, O. M., Mamán, L., & Navarro, G. (2020). New capabilities of Sentinel-2A/B satellites combined with in situ data for monitoring small harmful algal blooms in complex coastal waters. Scientific reports, 10, 1–14.

    Article  Google Scholar 

  • Caballero, I., & Navarro, G. (2021). Monitoring cyanoHABs and water quality in Laguna Lake (Philippines) with Sentinel-2 satellites during the 2020 Pacific typhoon season. Science of the Total Environment, 788, 147700.

    Article  CAS  Google Scholar 

  • 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. International Journal of Remote Sensing, 42, 4514–4535.

    Article  Google Scholar 

  • Carey, C. C., Ibelings, B. W., Hoffmann, E. P., Hamilton, D. P., & Brookes, J. D. (2012). Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate. Water research, 46, 1394–1407.

    Article  CAS  Google Scholar 

  • Carmichael, W. W., Azevedo, S., An, J. S., Molica, R., Jochimsen, E. M., Lau, S., Rinehart, K. L., Shaw, G. R., & Eaglesham, G. K. (2001). Human fatalities from cyanobacteria: chemical and biological evidence for cyanotoxins. Environmental health perspectives, 109, 663–668.

    Article  CAS  Google Scholar 

  • Carmichael, W. W., & Boyer, G. L. (2016). Health impacts from cyanobacteria harmful algae blooms: Implications for the North American Great Lakes. Harmful algae, 54, 194–212.

    Article  Google Scholar 

  • Chen, Q., Huang, M., Bai, K., & Li, X. (2020). An optimal two bands ratio model to monitor chlorophyll-a in urban lake using Landsat 8 data. In Page 02003 in E3S Web of Conferences. EDP Sciences.

    Google Scholar 

  • Chen, S., Han, L., Chen, X., Li, D., Sun, L., & Li, Y. (2015). Estimating wide range Total Suspended Solids concentrations from MODIS 250-m imageries: an improved method. ISPRS Journal of Photogrammetry and Remote Sensing, 99, 58–69.

    Article  Google Scholar 

  • Chen, Z., Xia, Y., Jiang, Y., Zhao, J., Wu, Y., & Li, J. (2021). Long-term observations of chlorophyll-a concentration in Honghu Lake using multi-source remote sensing data. In Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2021 (pp. 61–70). SPIE.

    Google Scholar 

  • Cheng, K. S., & Lei, T. C. (2001). Reservoir trophic state evaluation using Lanisat TM images. Journal of the American Water Resources Association, 37, 1321–1334.

    Article  CAS  Google Scholar 

  • Daly, R. I., Ho, L., & Brookes, J. D. (2007). Effect of chlorination on Microcystis aeruginosa cell integrity and subsequent microcystin release and degradation. Environmental Science & Technology, 41, 4447–4453.

    Article  CAS  Google Scholar 

  • Dave, A., Chaplot, N., Chander, S., Gujarati, A., Singh, R. P., Patel, H. M., & Patel, U. D. (2019, May). Assessment of water quality parameters for some inland water bodies of western India using Landsat 8 data. In World Environmental and Water Resources Congress 2019 (pp. 98-107). Reston, VA: American Society of Civil Engineers.

  • Davis, T. W., Berry, D. L., Boyer, G. L., & Gobler, C. J. (2009). The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful algae, 8(5), 715–725.

    Article  CAS  Google Scholar 

  • Deutsch, E. 2020. Reservoir eutrophication dynamics in semi-arid regions: assessing the role of excessive nutrient loading and temporal changes in climate forcing.

    Google Scholar 

  • Deutsch, E., I. Alameddine, and M. El-Fadel. 2014. Developing Landsat based algorithms to augment in situ monitoring of freshwater lakes and reservoirs.

    Google Scholar 

  • Deutsch, E., Alameddine, I., & El-Fadel, M. (2018). Monitoring water quality in a hypereutrophic reservoir using Landsat ETM+ and OLI sensors: how transferable are the water quality algorithms? Environmental Monitoring and Assessment, 190, 141.

    Article  Google Scholar 

  • Dolan, D. M., & Chapra, S. C. (2012). Great Lakes total phosphorus revisited: 1. Loading analysis and update (1994–2008). Journal of Great Lakes Research, 38, 730–740.

    Article  CAS  Google Scholar 

  • Domínguez Gómez, J., Chuvieco Salinero, E., & Sastre Merlín, A. (2009). Monitoring transparency in inland water bodies using multispectral images. International Journal of Remote Sensing, 30, 1567–1586.

    Article  Google Scholar 

  • Dörnhöfer, K., Göritz, A., Gege, P., Pflug, B., & Oppelt, N. (2016). Water constituents and water depth retrieval from Sentinel-2A—a first evaluation in an oligotrophic lake. Remote Sensing, 8, 941.

    Article  Google Scholar 

  • Dörnhöfer, K., & Oppelt, N. (2016). Remote sensing for lake research and monitoring–Recent advances. Ecological Indicators, 64, 105–122.

    Article  Google Scholar 

  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., & Martimort, P. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36.

    Article  Google Scholar 

  • Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8, 354.

    Article  Google Scholar 

  • Du, Z., Li, W., Zhou, D., Tian, L., Ling, F., Wang, H., Gui, Y., & Sun, B. (2014). Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote sensing letters, 5, 672–681.

    Article  Google Scholar 

  • El-Fadel, M., R. Maroun, R. Bsat, M. Makki, P. Reiss, and D. Rothberg. 2003. Water quality assessment of the upper Litani River basin and Lake Qaraoun—Lebanon. Integrated Water and Coastal Resources Management-Indefinite Quantity Contract. Bureau for Asia and the Near East. US Agency for International Development. 77p.

  • El-Nakib, S., Alameddine, I., Najm, M. A., & Massoud, M. (2018). Quantifying the spatio-temporal variability of water quality in an urbanizing perennial mediterranean river: The case of the Beirut river. WIT Transactions on Ecology and the Environment, 228, 187–197.

    Article  CAS  Google Scholar 

  • European Space Agency (ESA). 2017. Sen2Cor configuration and user manual. ACRI-ST. Sophia-Antipolis, France.

  • Fadel, A., Faour, G., & Slim, K. (2016). Assessment of The trophic state and Chlorophyll-a concentrations using Landsat OLI in Karaoun reservoir (p. 17). Lebanon.

    Google Scholar 

  • Fadel, A., Lemaire, B. J., Atoui, A., Vinçon-Leite, B., Amacha, N., Slim, K., & Tassin, B. (2014). First assessment of the ecological status of Karaoun reservoir, Lebanon. Lakes & Reservoirs: Research & Management, 19, 142–157.

    Article  CAS  Google Scholar 

  • Forghani, A., Islam, M., & Kazemi, S. (2021). Earth observation techniques to assess water quality monitoring in the Murray Darling Basin of Australia. World Journal of Geomatics and Geosciences, 1(1). Retrieved from https://www.scipublications.com/journal/index.php/wjgg/article/view/51

  • Free, G., Bresciani, M., Trodd, W., Tierney, D., O’Boyle, S., Plant, C., & Deakin, J. (2020). Estimation of lake ecological quality from Sentinel-2 remote sensing imagery. Hydrobiologia, 847, 1423–1438.

    Article  CAS  Google Scholar 

  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 19(1):1–67. https://doi.org/10.1214/aos/1176347963

  • Germán, A., Shimoni, M., Beltramone, G., Rodriguez, M. I., Munchiut, J., Bonansea, M., Scavuzzo, C. M., & Ferral, A. (2021). Space-time monitoring of water quality in an eutrophic reservoir using SENTINEL-2 data-a case study of San Roque, Argentina. Remote Sensing Applications: Society and Environment, 24, 100614. https://doi.org/10.1016/j.rsase.2021.100614

  • Gibson, G., Carlson, R., Simpson, J., Smeltzer, E., Gerritson, J., Chapra, S., Heiskary, S., Jones, J., & Kennedy, R. (2000). Nutrient criteria technical guidance manual: lakes and reservoirs. Washington, DC, USA.

    Google Scholar 

  • Gitelson, A. (1992). The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13, 3367–3373.

    Article  Google Scholar 

  • Gitelson, A., Gao, B.-C., Li, R.-R., Berdnikov, S., & Saprygin, V. (2011). Estimation of chlorophyll-a concentration in productive turbid waters using a Hyperspectral Imager for the Coastal Ocean—the Azov Sea case study. Environmental Research Letters, 6, 024023.

    Article  Google Scholar 

  • Gons, H. J., Auer, M. T., & Effler, S. W. (2008). MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes. Remote Sensing of Environment, 112, 4098–4106.

    Article  Google Scholar 

  • Govedarica, M., & Jakovljević, G. (2019). Monitoring spatial and temporal variation of water quality parameters using time series of open multispectral data. In Page 111740Y in Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019). International Society for Optics and Photonics.

    Google Scholar 

  • Grendaitė, D., Stonevičius, E., Karosienė, J., Savadova, K., & Kasperovičienė, J. (2018). Chlorophyll-a concentration retrieval in eutrophic lakes in Lithuania from Sentinel-2 data (p. 4). Geologija.

    Google Scholar 

  • Guo, H., Huang, J. J., Chen, B., Guo, X., & Singh, V. P. (2021). A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery. International Journal of Remote Sensing, 42, 1841–1866.

    Article  Google Scholar 

  • Gurlin, D., Gitelson, A. A., & Moses, W. J. (2011). Remote estimation of chl-a concentration in turbid productive waters—Return to a simple two-band NIR-red model? Remote Sensing of Environment, 115, 3479–3490.

    Article  Google Scholar 

  • Hijmans, R. J., J. Van Etten, J. Cheng, M. Mattiuzzi, M. Sumner, J. A. Greenberg, O. P. Lamigueiro, A. Bevan, E. B. Racine, and A. Shortridge. 2015. Package ‘raster’. R package.

    Google Scholar 

  • Horváth, H., Kovács, A. W., Riddick, C., & Présing, M. (2013). Extraction methods for phycocyanin determination in freshwater filamentous cyanobacteria and their application in a shallow lake. European Journal of Phycology, 48, 278–286.

    Article  Google Scholar 

  • Hu, C., Lee, Z., Ma, R., Yu, K., Li, D., & Shang, S. (2010). Moderate resolution imaging spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal of Geophysical Research: Oceans, 115(C4).

  • Huisman, J., Matthijs, H. C. P., & Visser, P. M. (2005). Harmful Cyanobacteria. Springer.

    Book  Google Scholar 

  • Hussein, N. M., & Assaf, M. N. (2020). Multispectral remote sensing utilization for monitoring chlorophyll-a levels in inland water bodies in Jordan. The Scientific World Journal, 2020, Article ID 5060969. https://doi.org/10.1155/2020/5060969

  • Hussein, N. M., Assaf, M. N., & Abohussein, S. S. (2023). Sentinel 2 analysis of turbidity retrieval models in inland water bodies: The case study of Jordanian dams. The Canadian Journal of Chemical Engineering, 101(3), 1171–1184.

    Article  CAS  Google Scholar 

  • Hyde, K. J., O’Reilly, J. E., & Oviatt, C. A. (2007). Validation of SeaWiFS chlorophyll a in Massachusetts Bay. Continental Shelf Research, 27, 1677–1691.

    Article  Google Scholar 

  • IARC. (2010). Ingested nitrate and nitrite, and cyanobacterial peptide toxins. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 94, v–vii.

    Google Scholar 

  • ILEC. (1993). 1988-1993 Survey of the State of the World’s Lakes. Volumes I-IV.

    Google Scholar 

  • Jaelani, L. M., Limehuwey, R., Kurniadin, N., Pamungkas, A., Koenhardono, E. S., & Sulisetyono, A. (2016). Estimation of total suspended sediment and chlorophyll-A concentration from Landsat 8-Oli: the effect of atmosphere and retrieval algorithm. IPTEK The Journal for Technology and Science, 27(1).

  • Jiang, Y., Ji, B., Wong, R., & Wong, M. H. (2008). Statistical study on the effects of environmental factors on the growth and microcystins production of bloom-forming cyanobacterium—Microcystis aeruginosa. Harmful algae, 7, 127–136.

    Article  CAS  Google Scholar 

  • Johansen, R. A., M. Reif, E. Emery, J. Nowosad, R. Beck, M. Xu, and H. Liu. 2019. waterquality: An open-source R package for the detection and quantification of cyanobacterial harmful algal blooms and water quality. Aquatic Nuisance Species Research Program, US Army Engineer Research and Development Center Environmental Laboratory, Vicksburg, MS 39180–6199.

  • Jurdi, M., Korfali, S. I., Karahagopian, Y., & Davies, B. E. (2002). Evaluation of water quality of the Qaraaoun Reservoir, Lebanon: Suitability for multipurpose usage. Environmental monitoring and assessment, 77, 11–30.

    Article  CAS  Google Scholar 

  • Kapsalis, V. C., & Kalavrouziotis, I. K. (2021). Eutrophication—a worldwide water quality issue (pp. 1–21). Chemical Lake Restoration. Springer.

    Google Scholar 

  • Karaoui, I., Arioua, A., Boudhar, A., Hssaisoune, M., El Mouatassime, S., Ouhamchich, K. A., Elhamdouni, D., Idrissi, A. E. A., & Nouaim, W. (2019). Evaluating the potential of Sentinel-2 satellite images for water quality characterization of artificial reservoirs: The Bin El Ouidane Reservoir case study (Morocco). Meteorology Hydrology and Water Management. Research and Operational Applications, 7, 31–39.

    Google Scholar 

  • Kisi, O., & Parmar, K. S. (2016). Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology, 534, 104–112.

    Article  CAS  Google Scholar 

  • Korfali, S. I., Jurdi, M., & Davies, B. E. (2006). Variation of metals in bed sediments of Qaraaoun Reservoir, Lebanon. Environmental monitoring and assessment, 115, 307–319.

    Article  CAS  Google Scholar 

  • Kosten, S., Huszar, V. L., Bécares, E., Costa, L. S., van Donk, E., Hansson, L. A., et al. (2012). Warmer climates boost cyanobacterial dominance in shallow lakes. Global Change Biology, 18(1), 118–126.

    Article  Google Scholar 

  • Kutser, T., Pierson, D. C., Tranvik, L., Reinart, A., Sobek, S., & Kallio, K. (2005). Using satellite remote sensing to estimate the colored dissolved organic matter absorption coefficient in lakes. Ecosystems, 8, 709–720.

    Article  Google Scholar 

  • Kyryliuk, D., & Kratzer, S. (2019). Evaluation of Sentinel-3A OLCI products derived using the Case-2 Regional CoastColour processor over the Baltic Sea. Sensors, 19, 3609.

    Article  Google Scholar 

  • Li, H. (2021). Evaluation of atmospheric correction methods for Sentinel-2 image—a case study of Poyang Lake. Spacecraft Recovery & Remote Sensing, 42(4), 108–119.

    Google Scholar 

  • Li, M., Dong, J., Zhang, Y., Yang, H., Van Zwieten, L., Lu, H., Alshameri, A., Zhan, Z., Chen, X., & Jiang, X. (2021). A critical review of methods for analyzing freshwater eutrophication. Water, 13, 225.

    Article  Google Scholar 

  • Li, S., Song, K., Wang, S., Liu, G., Wen, Z., Shang, Y., Lyu, L., Chen, F., Xu, S., & Tao, H. (2021). Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm. Science of the Total Environment, 778, 146271.

    Article  CAS  Google Scholar 

  • Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., Sun, B., & Zhang, X. (2013). A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI. Remote Sensing, 5, 5530–5549.

    Article  Google Scholar 

  • Li, X., Ding, J., & Ilyas, N. (2021). Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study. Water Supply, 21, 1291–1312.

    Article  Google Scholar 

  • Li, X., Janssen, A. B., de Klein, J. J., Kroeze, C., Strokal, M., Ma, L., & Zheng, Y. (2019). Modeling nutrients in Lake Dianchi (China) and its watershed. Agricultural Water Management, 212, 48–59.

    Article  Google Scholar 

  • Li, Y., J.-a. Chen, Q. Zhao, C. Pu, Z. Qiu, R. Zhang, and W. Shu. 2011. A cross-sectional investigation of chronic exposure to microcystin in relationship to childhood liver damage in the Three Gorges Reservoir Region, China. Environmental health perspectives 119, 1483-1488.

  • Liu, H., He, B., Zhou, Y., Yang, X., Zhang, X., Xiao, F., Feng, Q., Liang, S., Zhou, X., & Fu, C. (2021). Eutrophication monitoring of lakes in Wuhan based on Sentinel-2 data. GIScience & Remote Sensing, 1–23.

  • Liu, H., Li, Q., Shi, T., Hu, S., Wu, G., & Zhou, Q. (2017). Application of sentinel 2 MSI images to retrieve suspended particulate matter concentrations in Poyang Lake. Remote Sensing, 9, 761.

    Article  Google Scholar 

  • Liu, X., Lu, X., & Chen, Y. (2011). The effects of temperature and nutrient ratios on Microcystis blooms in Lake Taihu, China: an 11-year investigation. Harmful algae, 10, 337–343.

    Article  Google Scholar 

  • Liu, X., Steele, C., Simis, S., Warren, M., Tyler, A., Spyrakos, E., Selmes, N., & Hunter, P. (2021). Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs. Remote Sensing of Environment, 267, 112710.

    Article  Google Scholar 

  • Lobo, F. D. L., Nagel, G. W., Maciel, D. A., Carvalho, L. A. S. d., Martins, V. S., Barbosa, C. C. F., & Novo, E. M. L. D. M. (2021). AlgaeMAp: algae bloom monitoring application for inland waters in Latin America. Remote Sensing, 13, 2874.

    Article  Google Scholar 

  • Lone, Y., Koiri, R. K., & Bhide, M. (2015). An overview of the toxic effect of potential human carcinogen Microcystin-LR on testis. Toxicology Reports, 2, 289–296.

    Article  CAS  Google Scholar 

  • Long, S., Zhang, T., Fan, J., Li, C., & Xiong, K. (2020). Responses of phytoplankton functional groups to environmental factors in the Pearl River, South China. Environmental Science and Pollution Research, 27, 42242–42253.

    Article  CAS  Google Scholar 

  • Lonjou, V., Desjardins, C., Hagolle, O., Petrucci, B., Tremas, T., Dejus, M., Makarau, A., & Auer, S. (2016). Maccs-atcor joint algorithm (maja). In Page 1000107 in Remote Sensing of Clouds and the Atmosphere XXI. International Society for Optics and Photonics.

    Google Scholar 

  • Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., & Gascon, F. (2016). Sentinel-2 Sen2Cor: L2A processor for users. In Proceedings Living Planet Symposium 2016 (pp. 1–8). Spacebooks Online.

    Google Scholar 

  • Lürling, M., Meng, D., & Faassen, E. J. (2014). Effects of hydrogen peroxide and ultrasound on biomass reduction and toxin release in the cyanobacterium, Microcystis aeruginosa. Toxins, 6, 3260–3280.

    Article  Google Scholar 

  • Ma, J., Jin, S., Li, J., He, Y., & Shang, W. (2021). Spatio-temporal variations and driving forces of harmful algal blooms in Chaohu Lake: a multi-source remote sensing approach. Remote Sensing, 13, 427.

    Article  Google Scholar 

  • Ma, Y., Song, K., Wen, Z., Liu, G., Shang, Y., Lyu, L., Du, J., Yang, Q., Li, S., & Tao, H. (2021). Remote Sensing of Turbidity for Lakes in Northeast China Using Sentinel-2 Images with Machine Learning Algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 9132–9146.

  • Maciel, D. A., Barbosa, C. C. F., de Moraes Novo, E. M. L., Júnior, R. F., & Begliomini, F. N. (2021). Water clarity in Brazilian water assessed using Sentinel-2 and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 182, 134–152.

    Article  Google Scholar 

  • Maier, P. M., & Keller, S. (2018). Machine learning regression on hyperspectral data to estimate multiple water parameters. In in 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (pp. 1–5). IEEE.

    Google Scholar 

  • Maier, P. M., & Keller, S. (2019). Application of different simulated spectral data and machine learning to estimate the chlorophyll A concentration of several inland waters. In in 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) (pp. 1–5). IEEE.

    Google Scholar 

  • Markogianni, V., Dimitriou, E., & Karaouzas, I. (2014). Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications. Environmental Monitoring and Assessment, 186, 5009–5026.

    Article  CAS  Google Scholar 

  • Matthews, M. W., Bernard, S., & Winter, K. (2010). Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS. Remote sensing of environment, 114, 2070–2087.

    Article  Google Scholar 

  • Michalak, A. M., Anderson, E. J., Beletsky, D., Boland, S., Bosch, N. S., Bridgeman, T. B., Chaffin, J. D., Cho, K., Confesor, R., & Daloğlu, I. (2013). Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions. Proceedings of the National Academy of Sciences, 110, 6448–6452.

    Article  CAS  Google Scholar 

  • Milborrow, M. S. (2019). Package ‘earth’. R Software package.

    Google Scholar 

  • Mishra, D. R., Ogashawara, I., & Gitelson, A. A. (2017). Bio-optical modeling and remote sensing of inland waters. Elsevier.

    Google Scholar 

  • Mishra, S., & Mishra, D. R. (2012). Normalized difference chlorophyll index: a novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394–406.

    Article  Google Scholar 

  • Mohamed, M. N., Wellen, C., Parsons, C. T., Taylor, W. D., Arhonditsis, G., Chomicki, K. M., Boyd, D., Weidman, P., Mundle, S. O., & Cappellen, P. V. (2019). Understanding and managing the re-eutrophication of Lake Erie: knowledge gaps and research priorities. Freshwater Science, 38, 675–691.

    Article  Google Scholar 

  • Moses, W. J., Gitelson, A. A., Berdnikov, S., & Povazhnyy, V. (2009). Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data—successes and challenges. Environmental Research Letters, 4, 045005.

    Article  Google Scholar 

  • Moss, B. (2012). Cogs in the endless machine: lakes, climate change and nutrient cycles: a review. Science of the Total Environment, 434, 130–142.

    Article  CAS  Google Scholar 

  • Muhammetoglu, A., Muhammetoglu, H., Oktas, S., Ozgokcen, L., & Soyupak, S. (2005). Impact assessment of different management scenarios on water quality of Porsuk river and dam system–Turkey. Water Resources Management, 19, 199–210.

    Article  Google Scholar 

  • Nguyen, H. Q., Ha, N. T., Nguyen-Ngoc, L., & Pham, T. L. (2021). Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll-a content: A case study in the Tri An Reservoir Vietnam. Water Environment Research, 93(12), 2941–2957.

    Article  CAS  Google Scholar 

  • Odermatt, D., Pomati, F., Pitarch, J., Carpenter, J., Kawka, M., Schaepman, M., & Wüest, A. (2012). MERIS observations of phytoplankton blooms in a stratified eutrophic lake. Remote Sensing of Environment, 126, 232–239.

    Article  Google Scholar 

  • Olmanson, L. G., Bauer, M. E., & Brezonik, P. L. (2008). A 20-year Landsat water clarity census of Minnesota's 10,000 lakes. Remote sensing of environment, 112, 4086–4097.

    Article  Google Scholar 

  • Olmanson, L. G., Brezonik, P. L., & Bauer, M. E. (2011). Evaluation of medium to low resolution satellite imagery for regional lake water quality assessments. Water Resources Research, 47(9).

  • Olmanson, L. G., Brezonik, P. L., Finlay, J. C., & Bauer, M. E. (2016). Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes. Remote Sensing of Environment, 185, 119–128.

    Article  Google Scholar 

  • Otten, T., Xu, H., Qin, B., Zhu, G., & Paerl, H. (2012). Spatiotemporal patterns and ecophysiology of toxigenic Microcystis blooms in Lake Taihu, China: implications for water quality management. Environmental Science & Technology, 46, 3480–3488.

    Article  CAS  Google Scholar 

  • Owusu, C. K., Salama, M. S., Nyarko, B. K., & Asare, C. K. O. (2019). Capability of Landsat 8 and SPOT 6 in Quantifying Chlorophyll-a in Inland Lakes, Netherlands. ISPRS Journal of Photogrammetry and Remote Sensing, 28, 1021–1038.

    Google Scholar 

  • Paerl, H. W. & Huisman, J. (2008). Blooms like it hot vol. 320. Science, 320(5872), 57-58.

  • Pahlevan, N., Lee, Z., Wei, J., Schaaf, C. B., Schott, J. R., & Berk, A. (2014). On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing. Remote Sensing of Environment, 154, 272–284.

    Article  Google Scholar 

  • Palmer, S. C., Hunter, P. D., Lankester, T., Hubbard, S., Spyrakos, E., Tyler, A. N., Presing, M., Horvath, H., Lamb, A., & Balzter, H. (2015). Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake. Remote Sensing of Environment, 157, 158–169.

    Article  Google Scholar 

  • Palmer, S. C., Kutser, T., & Hunter, P. D. (2015). Remote sensing of inland waters: Challenges, progress and future directions. Elsevier.

    Google Scholar 

  • Pantoja, D. A., Vega-Álvarez, N. A., & Gasca-Ortiz, T. (2021). Trophic state in a tropical lake based on Chlorophyll-a profiler data and Sentinel-2 images: the onset of an algal bloom event. Water Environment Research. 93(10), 2185–2197.

  • Papenfus, M., Schaeffer, B., Pollard, A. I., & Loftin, K. (2020). Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs. Environmental Monitoring and Assessment, 192, 1–22.

    Article  Google Scholar 

  • Pereira, A. R. A., Lopes, J. B., & G. M. d. Espindola, and C. E. d. Silva. (2020). Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil. Revista Ambiente & Água, 15(2). https://doi.org/10.4136/ambi-agua.2488

  • Pinardi, M., Bresciani, M., Villa, P., Cazzaniga, I., Laini, A., Tóth, V., Fadel, A., Austoni, M., Lami, A., & Giardino, C. (2018). Spatial and temporal dynamics of primary producers in shallow lakes as seen from space: Intra-annual observations from Sentinel-2A. Limnologica, 72, 32–43.

    Article  Google Scholar 

  • Pizani, F. M., Maillard, P., Ferreira, A. F., & de Amorim, C. C. (2020). Estimation of water quality in a reservoir from Sentinel-2 MSI and Landsat-8 OLI sensors. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 3, 401-408.

  • Poddar, S., Chacko, N., & Swain, D. (2019). Estimation of chlorophyll-a in northern coastal Bay of Bengal using Landsat-8 OLI and Sentinel-2 MSI sensors. Frontiers in Marine Science, 6, 598.

    Article  Google Scholar 

  • Pompeo, M., Moschini-Carlos, V., Bitencourt, M. D., Soria-Perpinya, X., Vicente, E., & Delegido, J. (2021). Water quality assessment using Sentinel-2 imagery with estimates of chlorophyll a, Secchi disk depth, and Cyanobacteria cell number: the Cantareira System reservoirs (São Paulo, Brazil). Environmental Science and Pollution Research28, 34990-35011.

  • Potes, M., Rodrigues, G., Penha, A. M., Novais, M. H., Costa, M. J., Salgado, R., & Morais, M. M. (2018). Use of Sentinel 2–MSI for water quality monitoring at Alqueva reservoir, Portugal. Proceedings of the International Association of Hydrological Sciences, 380, 73–79.

    Article  CAS  Google Scholar 

  • Qian, S. S., Stow, C. A., Rowland, F. E., Liu, Q., Rowe, M. D., Anderson, E. J., Stumpf, R. P., & Johengen, T. H. (2021). Chlorophyll a as an indicator of microcystin: short-term forecasting and risk assessment in Lake Erie. Ecological Indicators, 130, 108055.

    Article  CAS  Google Scholar 

  • Qiu, T., Xie, P., Liu, Y., Li, G., Xiong, Q., Hao, L., & Li, H. (2009). The profound effects of microcystin on cardiac antioxidant enzymes, mitochondrial function and cardiac toxicity in rat. Toxicology, 257, 86–94.

    Article  CAS  Google Scholar 

  • R Core Team. 2015. R: a language and environment for statistical computing., R Foundation for Statistical Computing, .

    Google Scholar 

  • Rice, E., R. Baird, A. Eaton, and L. Clesceri. 2012. Standard methods for the examination of water and wastewater, Ame. Pub. Hea. Ass.(APHA), Ame. Wat. Wor. Ass.(AWWA), Wat. Envir. Fed.(WEF) 22.

  • Richter, R., Louis, J., & Müller-Wilm, U. (2012). Sentinel-2 MSI—Level 2A products algorithm theoretical basis document. European Space Agency,(Special Publication) ESA SP, 49, 1–72.

    Google Scholar 

  • Riddick, C. A., Hunter, P. D., Domínguez Gómez, J. A., Martinez-Vicente, V., Présing, M., Horváth, H., Kovács, A. W., Vörös, L., Zsigmond, E., & Tyler, A. N. (2019). Optimal cyanobacterial pigment retrieval from ocean colour sensors in a highly turbid, optically complex lake. Remote Sensing, 11, 1613.

    Article  Google Scholar 

  • Rodrigues, G., Potes, M., Costa, M. J., Novais, M. H., Penha, A. M., Salgado, R., & Morais, M. M. (2020). Temporal and spatial variations of Secchi depth and diffuse attenuation coefficient from Sentinel-2 MSI over a large reservoir. Remote Sensing, 12, 768.

    Article  Google Scholar 

  • Rodríguez, Y. C., El Anjoumi, A., Gómez, J. D., Pérez, D. R., & Rico, E. (2014). Using Landsat image time series to study a small water body in Northern Spain. Environmental Monitoring and Assessment, 186, 3511–3522.

    Google Scholar 

  • Rogalus, M. K., & Watzin, M. C. (2008). Evaluation of sampling and screening techniques for tiered monitoring of toxic cyanobacteria in lakes. Harmful algae, 7, 504–514.

    Article  CAS  Google Scholar 

  • Sakuno, Y., Maeda, A., Mori, A., Ono, S., & Ito, A. (2019). A simple red tide monitoring method using Sentinel-2 data for sustainable management of brackish lake Koyama-ike Japan. Water, 11, 1044.

    Article  CAS  Google Scholar 

  • Santos, D. A., Martinez, J., Harmel, T., Borges, H., & Roig, H. (2020). Evaluation of Sentinel-2/Msi imagery products level-2a obtained by three different atmospheric corrections for monitoring suspended sediments concentration in Madeira River, Brazil. In in 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) (pp. 207–212). IEEE.

    Chapter  Google Scholar 

  • Schaeffer, B. A., Schaeffer, K. G., Keith, D., Lunetta, R. S., Conmy, R., & Gould, R. W. (2013). Barriers to adopting satellite remote sensing for water quality management. International Journal of Remote Sensing, 34, 7534–7544.

    Article  Google Scholar 

  • Schindler, D. W., Carpenter, S. R., Chapra, S. C., Hecky, R. E., & Orihel, D. M. (2016). Reducing phosphorus to curb lake eutrophication is a success. Environmental Science & Technology, 50(17), 8923–8929.

    Article  CAS  Google Scholar 

  • Shaban, A., & Nassif, N. (2007). Pollution in Qaraaoun Lake, Central Lebanon. Journal of Environmental Hydrology, 15, 1–14.

    Google Scholar 

  • Sharaf, N., Bresciani, M., Giardino, C., Faour, G., Slim, K., & Fadel, A. (2019). Using Landsat and in situ data to map turbidity as a proxy of cyanobacteria in a hypereutrophic Mediterranean reservoir. Ecological informatics, 50, 197–206.

    Article  Google Scholar 

  • Sharda, V., Prasher, S., Patel, R., Ojasvi, P., & Prakash, C. (2008). Performance of multivariate adaptive regression splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data/Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données. Hydrological sciences journal, 53, 1165–1175.

    Article  Google Scholar 

  • Shi, K., Zhang, Y., Xu, H., Zhu, G., Qin, B., Huang, C., Liu, X., Zhou, Y., & Lv, H. (2015). Long-term satellite observations of microcystin concentrations in Lake Taihu during cyanobacterial bloom periods. Environmental Science & Technology, 49, 6448–6456.

    Article  CAS  Google Scholar 

  • Shi, K., Zhang, Y., Zhu, G., Qin, B., & Pan, D. (2018). Deteriorating water clarity in shallow waters: Evidence from long term MODIS and in-situ observations. International journal of applied earth observation and geoinformation, 68, 287–297.

    Article  Google Scholar 

  • Siegelman, H., and J. Kycia. 1978. Algal biliproteins. In ‘Handbook of phycological methods: physiological and biochemical methods’.(Eds JA Hellebust, JS Craigie) pp. 71–79. Cambridge University Press: Cambridge.

  • Simis, S. G., Peters, S. W., & Gons, H. J. (2005). Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnology and oceanography, 50, 237–245.

    Article  CAS  Google Scholar 

  • Slim, K., Fadel, A., Atoui, A., Lemaire, B. J., Vinçon-Leite, B., & Tassin, B. (2014). Global warming as a driving factor for cyanobacterial blooms in Lake Karaoun, Lebanon. Desalination and Water Treatment, 52, 2094–2101.

    Article  CAS  Google Scholar 

  • Smith, J. L., & Haney, J. F. (2006). Foodweb transfer, accumulation, and depuration of microcystins, a cyanobacterial toxin, in pumpkinseed sunfish (Lepomis gibbosus). Toxicon, 48, 580–589.

    Article  CAS  Google Scholar 

  • Smith, V. H., & Schindler, D. W. (2009). Eutrophication science: where do we go from here? Trends in Ecology & Evolution, 24(4), 201–207.

    Article  Google Scholar 

  • Son, S., & Wang, M. (2012). Water properties in Chesapeake Bay from MODIS-Aqua measurements. Remote Sensing of Environment, 123, 163–174.

    Article  Google Scholar 

  • Soomets, T., Uudeberg, K., Jakovels, D., Brauns, A., Zagars, M., & Kutser, T. (2020). Validation and comparison of water quality products in baltic lakes using sentinel-2 msi and sentinel-3 OLCI data. Sensors, 20, 742.

    Article  CAS  Google Scholar 

  • Sòria-Perpinyà, X., Urrego, E. P., Pereira-Sandoval, M., Ruiz-Verdú, A., Soria, J. M., Delegido, J., Vicente, E., & Moreno, J. (2020). Monitoring water transparency of a hypertrophic lake (the Albufera of València) using multitemporal Sentinel-2 satellite images. Limnetica, 39, 373–386.

    Article  Google Scholar 

  • Sòria-Perpinyà, X., Urrego, P., Pereira-Sandoval, M., Ruiz-Verdú, A., Peña, R., Soria, J. M., Delegido, J., Vicente, E., & Moreno, J. (2019). Monitoring the ecological state of a hypertrophic lake (Albufera of València, Spain) using multitemporal Sentinel-2 images. Limnetica, 38, 457–469.

    Article  Google Scholar 

  • Sòria-Perpinyà, X., Vicente, E., Urrego, P., Pereira-Sandoval, M., Ruíz-Verdú, A., Delegido, J., Soria, J. M., & Moreno, J. (2020). Remote sensing of cyanobacterial blooms in a hypertrophic lagoon (Albufera of València, Eastern Iberian Peninsula) using multitemporal Sentinel-2 images. Science of the Total Environment, 698, 134305.

    Article  Google Scholar 

  • Sòria-Perpinyà, X., Vicente, E., Urrego, P., Pereira-Sandoval, M., Tenjo, C., Ruíz-Verdú, A., Delegido, J., Soria, J. M., Peña, R., & Moreno, J. (2021). Validation of water quality monitoring algorithms for Sentinel-2 and Sentinel-3 in Mediterranean inland waters with in situ reflectance data. Water, 13, 686.

    Article  Google Scholar 

  • Stendera, S., Adrian, R., Bonada, N., Cañedo-Argüelles, M., Hugueny, B., Januschke, K., Pletterbauer, F., & Hering, D. (2012). Drivers and stressors of freshwater biodiversity patterns across different ecosystems and scales: a review. Hydrobiologia, 696, 1–28.

    Article  Google Scholar 

  • Sun, D., Hu, C., Qiu, Z., & Shi, K. (2015). Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: a case study in Lake Dianchi. Optics express, 23, 3055–3074.

    Article  CAS  Google Scholar 

  • Tavares, M. H., Lins, R. C., Harmel, T., Fragoso, C. R., Jr., Martínez, J.-M., & Motta-Marques, D. (2021). Atmospheric and sunglint correction for retrieving chlorophyll-a in a productive tropical estuarine-lagoon system using Sentinel-2 MSI imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 174, 215–236.

    Article  Google Scholar 

  • Tham, T. T., Hung, T. L., Thuy, T. T., Mai, V. T., Trinh, L. T., Hai, C. V., & Minh, T. B. (2021). Assessment of some water quality parameters in the Red River downstream, Vietnam by combining field monitoring and remote sensing method. Environmental Science and Pollution Research, 1–13.

  • Tian, S., Guo, H., Xu, W., Zhu, X., Wang, B., Zeng, Q., et al. (2022). Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms. Environmental Science and Pollution Research30(7), 18617-18630.

  • Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B., & Nõges, T. (2016). First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sensing, 8, 640.

    Article  Google Scholar 

  • Tóth, V. Z., Ladányi, M., & Jung, A. (2021). Adaptation and validation of a Sentinel-based chlorophyll-a retrieval software for the central European freshwater lake (pp. 1–10). Balaton. PFG–Journal of Photogrammetry.

    Google Scholar 

  • Vanhellemont, Q., & Ruddick, K. (2018). Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications. Remote Sensing of Environment, 216, 586–597.

    Article  Google Scholar 

  • Vanhellemont, Q., & Ruddick, K. (2021). Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters. Remote Sensing of Environment, 256, 112284.

    Article  Google Scholar 

  • Vapnik, V. (1999). The nature of statistical learning theory. 2nd edition. Springer science & business media. New York, USA.

  • Venables, W. N., & Ripley, B. D. (2013). Modern applied statistics with S-PLUS. Springer Science & Business Media. New York, USA. https://doi.org/10.1007/978-0-387-21706-2

  • Vinçon-Leite, B., & Casenave, C. (2019). Modelling eutrophication in lake ecosystems: a review. Science of the Total Environment, 651, 2985–3001.

    Article  Google Scholar 

  • Waite, J. N., & Mueter, F. J. (2013). Spatial and temporal variability of chlorophyll-a concentrations in the coastal Gulf of Alaska, 1998–2011, using cloud-free reconstructions of SeaWiFS and MODIS-Aqua data. Progress in Oceanography, 116, 179–192.

    Article  Google Scholar 

  • Wang, M., Yao, Y., Shen, Q., Gao, H., Li, J., Zhang, F., & Wu, Q. (2021). Time-series analysis of surface-water quality in Xiong’an New Area, 2016–2019. Journal of the Indian Society of Remote Sensing, 49, 857–872.

    Article  Google Scholar 

  • Wang, S., Li, J., Zhang, B., Spyrakos, E., Tyler, A. N., Shen, Q., Zhang, F., Kuster, T., Lehmann, M. K., & Wu, Y. (2018). Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index. Remote sensing of environment, 217, 444–460.

    Article  Google Scholar 

  • Warren, M. A., Simis, S. G., Martinez-Vicente, V., Poser, K., Bresciani, M., Alikas, K., Spyrakos, E., Giardino, C., & Ansper, A. (2019). Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters. Remote Sensing of Environment, 225, 267–289.

    Article  Google Scholar 

  • Warren, M. A., Simis, S. G., & Selmes, N. (2021). Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms. Remote Sensing of Environment, 265, 112651.

    Article  Google Scholar 

  • Watanabe, F., Alcântara, E., Bernardo, N., de Andrade, C., Gomes, A. C., & A. do Carmo, T. Rodrigues, and L. H. Rotta. (2019). Mapping the chlorophyll-a horizontal gradient in a cascading reservoirs system using MSI Sentinel-2A images. Advances in Space Research, 64, 581–590.

    Article  Google Scholar 

  • Whitton, B. A. (Ed.). (2012). Ecology of cyanobacteria II: their diversity in space and time. Springer Science & Business Media.

    Google Scholar 

  • WHO. (1996). Health criteria and other supporting information. Guidelines for Drinking-Water Quality, 2, 796–803.

    Google Scholar 

  • Wilson, E. K. (2014). Danger from microcystins in Toledo water unclear. Chemical & Engineering News, 92(32), 9.

  • Wu, S., Xie, P., Liang, G., Wang, S., & Liang, X. (2006). Relationships between microcystins and environmental parameters in 30 subtropical shallow lakes along the Yangtze River, China. Freshwater Biology, 51, 2309–2319.

    Article  CAS  Google Scholar 

  • Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, 3025–3033.

    Article  Google Scholar 

  • Zhang, R., Qi, F., Liu, C., Zhang, Y., Wang, Y., Song, Z., Kumirska, J., & Sun, D. (2019). Cyanobacteria derived taste and odor characteristics in various lakes in China: Songhua Lake, Chaohu Lake and Taihu Lake. Ecotoxicology and environmental safety, 181, 499–507.

    Article  CAS  Google Scholar 

  • Zhang, T., Lu, X., Yu, R., Qin, M., Wei, C., & Hong, S. (2020). Response of extracellular and intracellular alkaline phosphatase in Microcystis aeruginosa to organic phosphorus. Environmental Science and Pollution Research, 27, 42304–42312.

    Article  CAS  Google Scholar 

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Abbas, M., Alameddine, I. Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form. Environ Monit Assess 195, 923 (2023). https://doi.org/10.1007/s10661-023-11456-7

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