Research Article
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Evaluating the Performance of Algorithms in Estimating the Chl-a Concentration of Lake Bafa

Year 2022, , 30 - 38, 28.06.2022
https://doi.org/10.48053/turkgeo.1118373

Abstract

Monitoring and estimating pigment concentrations in water bodies have a critical role in early intervention or investigation of causes for prevention. Remote sensing data are the most effective alternative due to its advantages as effortless, requiring less labor, and displaying large areas in a single frame. Analyzing and estimating Chlorophyll-a (Chl-a) concentrations constitute the most important research topics in water bodies because all phytoplankton contain Chl-a. In this study, we evaluated the performance of algorithms in estimating the Chl-a concentration of Lake Bafa based on Sentinel 2 bands which are simulated from in-situ reflectance data. We used 1/R665xR705, 1/R665-1/R705, (1/R665-1/R705) x R740, R705/(R560+R665), so called M09, G09-2B, G09-3B, K07, respectively and Normalized Difference Chlorophyll Index (NDCI) algorithms for evaluation. Water samples and in-situ measurements were collected and obtained in two field campaigns. Bands of Sentinel 2 were then simulated from in-situ reflectance data and used to calibrate and validate models for Chl-a estimation. R² values of 0.679, 0.749, 0.395, 0.726, and RMSE values of 0.7 and 1.882, 1.663, 1.737, and 1.818 μg/L have been obtained for M09, G09-2B, G09-3B, K07, and NDCI algorithms, respectively. Sentinel 2 images have been used for map validation. Our results show that M09 and NDCI algorithms performed better in estimating Chl-a compared to the other three algorithms for our data range at Lake Bafa.

Supporting Institution

Konya Technical University Scientific Research Project Coordinatorship

Project Number

18201069

References

  • Beck, R., Xu, M., Zhan, S., Liu, H., Johansen, R. A., Tong, S., Yang, B., … & Huang, Y. (2017). Comparison of satellite reflectance algorithms for estimating phycocyanin values and cyanobacterial total biovolume in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sensing, 9(6), 538.
  • Falkowski, P. (2012). Ocean Science: the power of plankton. Nature, 483, 17–20.
  • Field, C.B., Behrenfeld, M.J., Randerson, J.T., & Falkowski, P. (1998). Primary production of the biosphere: integrating terrestrial and oceanic components. Science, 281(5374), 237-240.
  • Gitelson, A.A., Gurlin, D., Moses, W.J., & Barrow, T. (2009). A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters. Environmental Research Letters, 4(4), 045003.
  • Hepsogutlu, D. (2012). Macrobentic Organisms and Physicochemical Parameters of Bafa Lake (MSc Thesis). Dokuz Eylul University, İzmir, Turkey (In Turkish).
  • Hunter, P.D., Tyler, A.N., Gilvear, D.J., & Willby, N.J. (2009). Using remote sensing to aid the assessment of human health risks from blooms of potentially toxic cyanobacteria. Environmental science & technology, 43(7), 2627-2633.
  • Hunter, P.D., Tyler, A.N., Carvalho, L., Codd, G.A., & Maberly, S.C. (2010). Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sensing of Environment, 114(11), 2705-2718.
  • ISO. (1992). Water quality measurement of biochemical parameters spectrometric determination of the chlorophyll-a concentration. International Organization for Standardization.
  • Kesici, K. (2015). The Research on the Factors that Causes Toxic Nodularia Spumigena Mertens ex Bornet & Flahault Blooms in Lake Bafa (PhD Thesis). Ege University, İzmir, Turkey (In Turkish).
  • Kızılkaya, I.T., Demirel, Z., Kesici, K., Kesici, E., & Sukatar, A. (2016). Morphological, Molecular and Toxicologial Characterization of Nodularia spumigena Mertens in Jungens (1822) from Brackishwater Lake Bafa (Turkey). Sinop Uni J Nat Sci, 1, 39–52.
  • Koçak, F., Aydın-Önen, S., Açık, Ş., & Küçüksezgin, F. (2017). Seasonal and spatial changes in water and sediment quality variables in Bafa Lake. Environmental Earth Sciences, 76(17), 1-11.
  • Koponen, S., Attila, J., Pulliainen, J., Kallio, K., Pyhaïahti, T., Lindfors, A., Rasmus, K., & Hallikainen, M. (2007). A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland. Continental Shelf Research, 27(2), 228-244.
  • Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S., & Torres-Perez, J. (2015). Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sensing of Environment, 167, 196–205.
  • Matthews, M.W., & Odermatt, D. (2015). Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sensing of Environment, 156, 374–382.
  • 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.
  • Mishra, S., Mishra, D.R., Lee, Z., & Tucker, C.S. (2013). Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach. Remote Sensing of Environment, 133, 141-151.
  • Mobley, C.D. (1999). Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38(36), 7442–7455.
  • Moses, W.J., Saprygin, V., Gerasyuk, V., Povazhnyy, V., Berdnikov, S., & Gitelson, A.A. (2019). Olci-based nir-red models for estimating chlorophyll-a concentration in productive coastal waters - a preliminary evaluation. Environmental Research Communications, 1(1) 011002.
  • Ozturk, Y. (2018). Determination of Population Density and Propagation Map of Mytilaster Marioni (Locard, 1889) (Mollusca; Bivalvia; Mytilidae) in Lake Bafa (MSc thesis). Aydın Adnan Menderes University, Aydın, Turkey (In Turkish).
  • Paerl, H.W., Hall, N.S., & Calandrino, E.S. (2011). Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Science of the total environment, 409(10), 1739-1745.
  • Ruiz-Verdú, A., Simis, S.G.H., de Hoyos, C., Gons, H.J., & Peña-Martínez, R. (2008). An evaluation of algorithms for the remote sensing of cyanobacterial biomass. Remote Sensing of Environment, 112(11), 3996-4008
  • Shi, K., Zhang, Y., Li, Y., Li, L., Lv, H., & Liu, X. (2014). Remote estimation of cyanobacteria-dominance in inland waters. Water research, 68, 217-226.
  • Soja-Woźniak, M., Craig, S.E., Kratzer, S., Wojtasiewicz, B., Darecki, M., & Jones, C.T. (2017). A novel statistical approach for ocean colour estimation of inherent optical properties and cyanobacteria abundance in optically complex waters. Remote Sensing, 9(4), 343.
  • Vinh, P.Q., Ha, N.T.T., Binh, N.T., Thang, N. N., Oanh, L. T., & Thao, N.T.P. (2019). Developing algorithm for estimating chlorophyll-a concentration in the Thac Ba Reservoir surface water using Landsat 8 Imagery. Vietnam Journal of Earth Sciences, 41(1), 10-20.
  • Watanabe, F., Alcântara, E., Rodrigues, T., Rotta, L., Bernardo, N., & Imai, N. (2018). Remote sensing of the chlorophyll-a based on OLI/Landsat-8 and MSI/Sentinel-2a (Barra Bonita Reservoir, Brazil). Anais Da Academia Brasileira de Ciencias, 90(2), 1987–2000.
Year 2022, , 30 - 38, 28.06.2022
https://doi.org/10.48053/turkgeo.1118373

Abstract

Project Number

18201069

References

  • Beck, R., Xu, M., Zhan, S., Liu, H., Johansen, R. A., Tong, S., Yang, B., … & Huang, Y. (2017). Comparison of satellite reflectance algorithms for estimating phycocyanin values and cyanobacterial total biovolume in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sensing, 9(6), 538.
  • Falkowski, P. (2012). Ocean Science: the power of plankton. Nature, 483, 17–20.
  • Field, C.B., Behrenfeld, M.J., Randerson, J.T., & Falkowski, P. (1998). Primary production of the biosphere: integrating terrestrial and oceanic components. Science, 281(5374), 237-240.
  • Gitelson, A.A., Gurlin, D., Moses, W.J., & Barrow, T. (2009). A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters. Environmental Research Letters, 4(4), 045003.
  • Hepsogutlu, D. (2012). Macrobentic Organisms and Physicochemical Parameters of Bafa Lake (MSc Thesis). Dokuz Eylul University, İzmir, Turkey (In Turkish).
  • Hunter, P.D., Tyler, A.N., Gilvear, D.J., & Willby, N.J. (2009). Using remote sensing to aid the assessment of human health risks from blooms of potentially toxic cyanobacteria. Environmental science & technology, 43(7), 2627-2633.
  • Hunter, P.D., Tyler, A.N., Carvalho, L., Codd, G.A., & Maberly, S.C. (2010). Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sensing of Environment, 114(11), 2705-2718.
  • ISO. (1992). Water quality measurement of biochemical parameters spectrometric determination of the chlorophyll-a concentration. International Organization for Standardization.
  • Kesici, K. (2015). The Research on the Factors that Causes Toxic Nodularia Spumigena Mertens ex Bornet & Flahault Blooms in Lake Bafa (PhD Thesis). Ege University, İzmir, Turkey (In Turkish).
  • Kızılkaya, I.T., Demirel, Z., Kesici, K., Kesici, E., & Sukatar, A. (2016). Morphological, Molecular and Toxicologial Characterization of Nodularia spumigena Mertens in Jungens (1822) from Brackishwater Lake Bafa (Turkey). Sinop Uni J Nat Sci, 1, 39–52.
  • Koçak, F., Aydın-Önen, S., Açık, Ş., & Küçüksezgin, F. (2017). Seasonal and spatial changes in water and sediment quality variables in Bafa Lake. Environmental Earth Sciences, 76(17), 1-11.
  • Koponen, S., Attila, J., Pulliainen, J., Kallio, K., Pyhaïahti, T., Lindfors, A., Rasmus, K., & Hallikainen, M. (2007). A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland. Continental Shelf Research, 27(2), 228-244.
  • Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S., & Torres-Perez, J. (2015). Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sensing of Environment, 167, 196–205.
  • Matthews, M.W., & Odermatt, D. (2015). Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sensing of Environment, 156, 374–382.
  • 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.
  • Mishra, S., Mishra, D.R., Lee, Z., & Tucker, C.S. (2013). Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach. Remote Sensing of Environment, 133, 141-151.
  • Mobley, C.D. (1999). Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38(36), 7442–7455.
  • Moses, W.J., Saprygin, V., Gerasyuk, V., Povazhnyy, V., Berdnikov, S., & Gitelson, A.A. (2019). Olci-based nir-red models for estimating chlorophyll-a concentration in productive coastal waters - a preliminary evaluation. Environmental Research Communications, 1(1) 011002.
  • Ozturk, Y. (2018). Determination of Population Density and Propagation Map of Mytilaster Marioni (Locard, 1889) (Mollusca; Bivalvia; Mytilidae) in Lake Bafa (MSc thesis). Aydın Adnan Menderes University, Aydın, Turkey (In Turkish).
  • Paerl, H.W., Hall, N.S., & Calandrino, E.S. (2011). Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Science of the total environment, 409(10), 1739-1745.
  • Ruiz-Verdú, A., Simis, S.G.H., de Hoyos, C., Gons, H.J., & Peña-Martínez, R. (2008). An evaluation of algorithms for the remote sensing of cyanobacterial biomass. Remote Sensing of Environment, 112(11), 3996-4008
  • Shi, K., Zhang, Y., Li, Y., Li, L., Lv, H., & Liu, X. (2014). Remote estimation of cyanobacteria-dominance in inland waters. Water research, 68, 217-226.
  • Soja-Woźniak, M., Craig, S.E., Kratzer, S., Wojtasiewicz, B., Darecki, M., & Jones, C.T. (2017). A novel statistical approach for ocean colour estimation of inherent optical properties and cyanobacteria abundance in optically complex waters. Remote Sensing, 9(4), 343.
  • Vinh, P.Q., Ha, N.T.T., Binh, N.T., Thang, N. N., Oanh, L. T., & Thao, N.T.P. (2019). Developing algorithm for estimating chlorophyll-a concentration in the Thac Ba Reservoir surface water using Landsat 8 Imagery. Vietnam Journal of Earth Sciences, 41(1), 10-20.
  • Watanabe, F., Alcântara, E., Rodrigues, T., Rotta, L., Bernardo, N., & Imai, N. (2018). Remote sensing of the chlorophyll-a based on OLI/Landsat-8 and MSI/Sentinel-2a (Barra Bonita Reservoir, Brazil). Anais Da Academia Brasileira de Ciencias, 90(2), 1987–2000.
There are 25 citations in total.

Details

Primary Language English
Subjects Geological Sciences and Engineering (Other)
Journal Section Research Articles
Authors

Elif Kırtıloğlu 0000-0002-8449-2588

Hakan Karabörk 0000-0001-7387-7004

Project Number 18201069
Publication Date June 28, 2022
Submission Date May 18, 2022
Acceptance Date May 30, 2022
Published in Issue Year 2022

Cite

APA Kırtıloğlu, E., & Karabörk, H. (2022). Evaluating the Performance of Algorithms in Estimating the Chl-a Concentration of Lake Bafa. Turkish Journal of Geosciences, 3(1), 30-38. https://doi.org/10.48053/turkgeo.1118373