Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Overview of Chlorophyll-a Concentration Retrieval Algorithms from Multi-Satellite Data

  • Park, Ji-Eun (Department of Science Education, Seoul National University) ;
  • Park, Kyung-Ae (Department of Earth Science Education/Research Institute of Oceanography, Seoul National University) ;
  • Park, Young-Je (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology) ;
  • Han, Hee-Jeong (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology)
  • Received : 2019.08.10
  • Accepted : 2019.08.26
  • Published : 2019.08.31
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Abstract

Since the Coastal Zone Color Scanner (CZCS)/Nimbus-7 was launched in 1978, a variety of studies have been conducted to retrieve ocean color variables from multi-satellites. Several algorithms and formulations have been suggested for estimating ocean color variables based on multi band data at different wavelengths. Chlorophyll-a (chl-a) concentration is one of the most important variables to understand low-level ecosystem in the ocean. To retrieve chl-a concentrations from the satellite observations, an appropriate algorithm depending on water properties is required for each satellite sensor. Most operational empirical algorithms in the global ocean have been developed based on the band-ratio approach, which has the disadvantage of being more adapted to the open ocean than to coastal areas. Alternative algorithms, including the semi-analytical approach, may complement the limits of band-ratio algorithms. As more sensors are planned by various space agencies to monitor the ocean surface, it is expected that continuous monitoring of oceanic ecosystems and environments should be conducted to contribute to the understanding of the oceanic biosphere and the impact of climate change. This study presents an overview of the past and present algorithms for the estimation of chl-a concentration based on multi-satellite data and also presents the prospects for ongoing and upcoming ocean color satellites.

Keywords

References

  1. Aiken, J., Moore, G.F. Trees, C.C., Hooker, S.B., and Clark, D.K., 1995, The SeaWiFS CZCS-type pigment algorithm. In Hooker, S.B. and Firestone, E.R. (eds.), SeaWiFS technical report series Vol. 29. NASA Goddard Space Flight Center, Maryland, USA, 34 p.
  2. Aiken, J., Moore, G.F., and Holligan, P.M., 1992, Remote sensing of oceanic biology in relation to global climate change. Journal of Phycology, 28, 579-590. https://doi.org/10.1111/j.0022-3646.1992.00579.x
  3. Babin, S.M., Carton, J.A., Dickey, T.D., and Wiggert, J.D., 2007, Satellite evidence of hurricane-induced phytoplankton blooms in an oceanic desert. Journal of Geophysical Research, 109, C03043.
  4. Blondeau-Patissier, D., Gower, J.F., Dekker, A.G., Phinn, S.R., and Brando, V.E., 2014, A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Progress in Oceanography, 123, 123-144. https://doi.org/10.1016/j.pocean.2013.12.008
  5. Boucher, J., Weathers, K.C., Norouzi, H., and Steele, B., 2018, Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring. Ecological applications, 28(4), 1044-1054. https://doi.org/10.1002/eap.1708
  6. Bowers, D.G., Harker, G.E.L., and Stephan, B., 1996, Absorption spectra of inorganic particles in the Irish Sea and their relevance to remote sensing of chlorophyll. International Journal of Remote Sensing, 17(12), 2449-2460. https://doi.org/10.1080/01431169608948782
  7. Bricaud, A., Babin, M., Morel, A., and Claustre, H., 1995, Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization. Journal of Geophysical Research: Oceans, 100(C7), 13321-13332. https://doi.org/10.1029/95JC00463
  8. Bukata, R.P., Jerome, J.H., Kondratyev, A.S., and Pozdnyakov, D.V., 2018, Optical properties and remote sensing of inland and coastal waters. CRC Press, Boca Raton, USA, 384 p.
  9. Carder, K.L., Chen, F.R., Cannizzaro, J.P., Campbell, J.W., and Mitchell, B.G., 2004, Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a. Advances in Space Research, 33(7), 1152-1159. https://doi.org/10.1016/S0273-1177(03)00365-X
  10. Carder, K.L., Chen, F.R., Lee, Z.P., Hawes, S.K., and Kamykowski, D., 1999, Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitratedepletion temperatures. Journal of Geophysical Research: Oceans, 104(C3), 5403-5421. https://doi.org/10.1029/1998JC900082
  11. Carder, K.L., Hawes, S.K., Baker, K.A., Smith, R.C., Steward, R.G., and Mitchell, B.G., 1991, Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products. Journal of Geophysical Research: Oceans, 96(C11), 20599-20611. https://doi.org/10.1029/91JC02117
  12. Clarke, G.L., Ewing, G.C., and Lorenzen, C.J., 1970, Spectra of backscattered light from the sea obtained from aircraft as a measure of chlorophyll concentration. Science, 167, 1119-1121. https://doi.org/10.1126/science.167.3921.1119
  13. Coste, P., Larnaudie, F., Luquet, P., Heo, H., Jung, J., Kang, G., ... and Park, Y.J., 2017, Development of the new generation of geostationary ocean color imager. In Proceedings of the International Conference on Space Optics 2016. International Society for Optics and Photonics, Biarritz, France, 105620D.
  14. Darecki, M. and Stramski, D., 2004, An Evaluation of MODIS and SeaWiFS Bio-Optical Algorithms in the Baltic Sea. Remote sensing of Environment, 89, 326-350. https://doi.org/10.1016/j.rse.2003.10.012
  15. Dierssen, H.M., 2010, Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate. Proceedings of the National Academy of Sciences of the United States of America, 107(40), 17073-17078.
  16. Doerffer, R. and Schiller, H., 2007, The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28(3-4), 517-535. https://doi.org/10.1080/01431160600821127
  17. Doney, S.C., Glover, D.M., McCue, S.J., and Fuentes, M., 2003, Mesoscale variability of sea-viewing wide fieldof- view sensor (SeaWiFS) satellite ocean color: Global patterns and spatial scales. Journal of Geophysical Research, 108(C2), 3024.
  18. Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M. H., Femenias, P., Frerick, J., ... and Nieke, J., 2012, The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sensing of Environment, 120, 37-57. https://doi.org/10.1016/j.rse.2011.07.024
  19. El-Habashi, A., Duran, C.M., Lovko, V., Tomlinson, M.C., Stumpf, R.P., and Ahmed, S., 2017, Satellite retrievals of Karenia brevis harmful algal blooms in the West Florida shelf using neural networks and impacts of temporal variabilities. Journal of Applied Remote Sensing, 11(3), 032408. https://doi.org/10.1117/1.JRS.11.032408
  20. Falkowski, P. and Kiefer, D.A., 1985, Chlorophyll a fluorescence in phytoplankton: relationship to photosynthesis and biomass. Journal of Plankton Research, 7(5), 715-731. https://doi.org/10.1093/plankt/7.5.715
  21. Feldman, G., Kuring, N., Ng, C., Esaias, W., McClain, C., Elrod, J., ... and Walsh, S., 1989, Ocean color: Availability of the global data set. Eos, Transactions American Geophysical Union, 70(23), 634-641. https://doi.org/10.1029/89EO00184
  22. Franz, B.A., Kwiatowska, E.J., Meister, G., and McClain, C.R., 2008, Moderate Resolution Imaging Spectroradiometer on Terra: limitations for ocean color applications. Journal of Applied Remote Sensing, 2(1), 023525. https://doi.org/10.1117/1.2957964
  23. Garcia, C.A.E., Garcia, V.M.T., and McClain, C.R., 2005, Evaluation of SeaWiFS chlorophyll algorithms in the Southwestern Atlantic and Southern Oceans. Remote Sensing of Environment, 95(1), 125-137. https://doi.org/10.1016/j.rse.2004.12.006
  24. Garver, S.A. and Siegel, D.A., 1997, Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea. Journal of Geophysical Research: Oceans, 102(C8), 18607-18625. https://doi.org/10.1029/96JC03243
  25. Gitelson, A., Gurlin, D., Moses, W.J., and 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. https://doi.org/10.1088/1748-9326/4/4/045003
  26. Gitelson, A., Karnieli, A., Goldman, N., Yacobi, Y.Z., and Mayo, M., 1996, Chlorophyll estimation in the Southeastern Mediterranean using CZCS images: Adaptation of an algorithm and its validation. Journal of Marine Systems, 9(3-4), 283-290. https://doi.org/10.1016/S0924-7963(95)00047-X
  27. Gordon, H. and Morel, A., 1983, Lecture notes on coastal and estuarine studies. In Remote assessment of ocean color for interpretation of satellite visible imagery: A review Vol. 4. Springer-Verlag, NY, USA, 114 p.
  28. Gower, J., King, S., Borstad, G., and Brown, L., 2005, Detection of intense plankton blooms using the 709nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 26(9), 2005-2012. https://doi.org/10.1080/01431160500075857
  29. Gower, J., Brown, L., and Borstad, G., 2004, Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Canadian Journal of Remote Sensing, 30(1), 17-25. https://doi.org/10.5589/m03-048
  30. Gregg, W.W. and Conkright, M.E., 2001, Global seasonal climatologies of ocean chlorophyll: Blending in situ and satellite data for the Coastal Zone Color Scanner era. Journal of Geophysical Research: Oceans, 106(C2), 2499-2515. https://doi.org/10.1029/1999JC000028
  31. Guallar, C., Delgado, M., Diogene, J., and Fernandez-Tejedor, M., 2016, Artificial neural network approach to population dynamics of harmful algal blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo-nitzschia. Ecological Modelling, 338, 37-50. https://doi.org/10.1016/j.ecolmodel.2016.07.009
  32. Harley, C.D., Randall Hughes, A., Hultgren, K.M., Miner, B.G., Sorte, C.J., Thornber, C.S., ... and Williams, S.L., 2006, The impacts of climate change in coastal marine systems. Ecology Letters, 9(2), 228-241. https://doi.org/10.1111/j.1461-0248.2005.00871.x
  33. Hattab, T., Jamet, C., Sammari, C., and Lahbib, S., 2013, Validation of chlorophyll-${\alpha}$ concentration maps from Aqua MODIS over the Gulf of Gabes (Tunisia): Comparison between MedOC3 and OC3M bio-optical algorithms. International Journal of Remote Sensing, 34(20), 7163-7177. https://doi.org/10.1080/01431161.2013.815820
  34. Hoegh-Guldberg, O. and Bruno, J.F., 2010, The impact of climate change on the world's marine ecosystems. Science, 328(5985), 1523-1528. https://doi.org/10.1126/science.1189930
  35. Hooker, S.B., Firestone, E.R., Esaias, W.E., Feldman, G.C., Gregg, W.W., and Mcclain, C.R., 1992, An overview of SeaWiFS and ocean color. In Hooker, S.B. and Firestone, E.R. (eds.), SeaWiFS technical report series Vol. 1. NASA Goddard Space Flight Center, Maryland, USA, 24 p.
  36. Hovis, W.A., 1981, The Nimbus-7 coastal zone color scanner (CZCS) program. In Oceanography from space. Springer, MA, USA, 213-225.
  37. Hu, C. and Campbell, J., 2014, Oceanic chlorophyll-a content. In Biophysical applications of satellite remote sensing. Springer, Berlin, Germany, 171-203.
  38. Hu, C., Carder, K.L., and Muller-Karger, F.E., 2000, Atmospheric correction of SeaWiFS imagery over turbid coastal waters: a practical method. Remote sensing of environment, 74(2), 195-206. https://doi.org/10.1016/S0034-4257(00)00080-8
  39. Hu, C., Feng, L., Lee, Z.P., Franz, B.A., Bailey, S.W., Werdell, P.J., and Proctor, C.W., 2019, Improving satellite global chlorophyll a data products through algorithm refinement and data recovery. Journal of Geophysical Research-Oceans, 124(3), 1524-1543. https://doi.org/10.1029/2019JC014941
  40. Hu, C., Lee, Z., and Franz, B., 2012, Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1).
  41. Hu, C., Muller-Karger, F.E., Taylor, C.J., Carder, K.L., Kelble, C., Johns, E., and Heil, C.A., 2005, Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sensing of Environment, 97(3), 311-321. https://doi.org/10.1016/j.rse.2005.05.013
  42. IOCCG, 2006, Remote Sensing of Inherent Optical Properties: Fundamentals, tests of algorithms, and applications. In Lee, Z.-P. (ed.), Reports of the International Ocean-Colour Coordinating Group No. 5. Dartmouth, Canada, 129 p.
  43. IOCCG, 2007, Ocean-Colour Data Merging. In Gregg, W. (ed.), Reports of the International Ocean-Colour Coordinating Group No. 6. Dartmouth, Canada, 68 p.
  44. IOCCG, 2008, Why ocean colour? The societal benefits of ocean-colour technology. In Platt, T., Hoepffner, N., Stuart, V., and Brown, C. (eds.), Reports of the International Ocean-Colour Coordinating Group No. 7. Dartmouth, Canada, 141 p.
  45. IOCCG, 2012, Ocean-colour observations from a geostationary orbit. In Antoine, D. (ed.), Reports of the International Ocean Colour Coordinating Group No. 7. Dartmouth, Canada, 103 p.
  46. Kahru, M. and Mitchell, B.G., 1999, Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current. International Journal of Remote Sensing, 20(17), 3423-3429. https://doi.org/10.1080/014311699211453
  47. Kim, W., Moon, J.E., Park, Y.-J., and Ishizaka, J., 2016, Evaluation of chlorophyll retrievals from Geostationary Ocean Color Imager (GOCI) for the North-East Asian region. Remote Sensing of Environment, 184, 482-495. https://doi.org/10.1016/j.rse.2016.07.031
  48. Klemas, V., 2011, Remote sensing of coastal plumes and ocean fronts: overview and case study. Journal of Coastal Research, 28(1A), 1-7.
  49. Kratzer, S., Harvey, E.T., and Philipson, P., 2014, The use of ocean color remote sensing in integrated coastal zone management-A case study from Himmerfjarden, Sweden. Marine Policy, 43, 29-39. https://doi.org/10.1016/j.marpol.2013.03.023
  50. Kwiatkowska, E.J. and Fargion, G.S., 2003, Application of machine learning techniques towards the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data. IEEE Transactions on Geoscience and Remote Sensing 41, 2844-2860. https://doi.org/10.1109/TGRS.2003.818016
  51. Lee, S. and Lee, D., 2018, Four major South Korea's rivers using deep learning models. International Journal of Environmental Research and Public Health, 15(7), 1322. https://doi.org/10.3390/ijerph15071322
  52. Lee, Z., Carder, K.L., and Arnone, R.A., 2002, Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41(27), 5755-5772. https://doi.org/10.1364/AO.41.005755
  53. Lim, H., Choi, M., Kim, J., Kasai, Y., and Chan, P., 2018, AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, validation and merged products. Remote Sensing, 10(5), 699. https://doi.org/10.3390/rs10050699
  54. Magnuson, A., Harding, L.W., Mallonee, M.E., and Adolf, J.E., 2004, Bio-optical model for Chesapeake Bay and Middle Atlantic Bight. Estuarine, Coastal and Shelf Science, 61, 403-424. https://doi.org/10.1016/j.ecss.2004.06.020
  55. Maritorena, S., Siegel, D.A., and Peterson, A.R., 2002, Optimization of a semi analytical ocean color model for global-scale applications. Applied Optics, 41, 2705-2714. https://doi.org/10.1364/AO.41.002705
  56. Maritorena, S. and Siegel, D.A., 2005, Consistent merging of satellite ocean color data sets using a bio-optical model. Remote Sensing of Environment 94, 429-440. https://doi.org/10.1016/j.rse.2004.08.014
  57. Martinez, E., Antoine, D., D'Ortenzio, F., and Gentili, B., 2009, Climate-driven basin-scale decadal oscillations of oceanic phytoplankton. Science, 326(5957), 1253-1256. https://doi.org/10.1126/science.1177012
  58. McClain, C.R., 1993, Review of major CZCS applications: U.S. case studies. In Barale, V. and Schlittenhardt, P.M. (eds.), Ocean Colour: Theory and applications in a decade of CZCS experience. Eurocourses: Remote Sensing Vol. 3, Springer, Dordrecht, Netherlands, 167-188.
  59. McClain, C.R., 2009, A decade of satellite ocean color observations. Annual Review of Marine Science, 1, 19-42. https://doi.org/10.1146/annurev.marine.010908.163650
  60. Mitchell, B.G., 1994, Coastal zone color scanner retrospective. Journal of Geophysical Research, 99, 7291-7292. https://doi.org/10.1029/93JC03259
  61. Moon, J.E., Park, Y.J., Ryu, J.H., Choi, J.K., Ahn, J.H., Min, J.E., ... and Ahn, Y.H., 2012, Initial validation of GOCI water products against in situ data collected around Korean peninsula for 2010-2011. Ocean Science Journal, 47(3), 261-277. https://doi.org/10.1007/s12601-012-0027-1
  62. Morel, A. and Prieur, L., 1977, Analysis of variations in ocean color 1. Limnology and Oceanography, 22(4), 709-722. https://doi.org/10.4319/lo.1977.22.4.0709
  63. Moses, W.J., Gitelson, A.A., Berdnikov, S., and Povazhnyy, V., 2009, Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data-successes and challenges. Environmental Research Letters, 4(4), 045005. https://doi.org/10.1088/1748-9326/4/4/045005
  64. Murakami, H., 2016, Ocean color estimation by Himawari-8/AHI. In Proceedings of SPIE Asia-Pacific Remote Sensing. International Society for Optics and Photonics, 2016, New Delhi, India, 987810.
  65. Neville, R.A. and Gower, J.F.R., 1977, Passive remote sensing of phytoplankton via chlorophyll ${\alpha}$ fluorescence. Journal of Geophysical Research, 82(24), 3487-3493. https://doi.org/10.1029/JC082i024p03487
  66. O'Reilly, J.E. and Werdell, P.J., 2019, Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment, 229, 32-47. https://doi.org/10.1016/j.rse.2019.04.021
  67. O'Reilly, J.E., Maritorena, S., Mitchell, B.G., Siegel, D.A., Carder, K.L., Garver, S.A., Kahru, M., and McClain, C., 1998, Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research, 103, 24937-24953. https://doi.org/10.1029/98JC02160
  68. O'Reilly, J.E., Maritorena, S., Siegel, D.A., O'Brien, M.C., Toole, D., Mitchell, B.G., ... and Hooker, S.B., 2000, SeaWiFS postlaunch calibration and validation analyses, Part, 3. In Hooker, S.B. and Firestone, E.R. (eds.), NASA Technical Memorandum 2000-206892. NASA Goddard Space Flight Center, Maryland, USA, 49 p.
  69. Park, J.-E., Park, K.-A., Ullman, D. Cornillon, P., and Park, Y.-J., 2016, Observation of diurnal variations in mesoscale eddy sea-surface currents using GOCI data. Remote Sensing Letters, 7(12), 1131-1140. https://doi.org/10.1080/2150704X.2016.1219423
  70. Park, K.-A., Lee, M.-S., Park, J.-E., Ullman, D., Cornillon, P., and Park, Y.-J., 2018, Surface currents from hourly variations of suspended particulate matter from Geostationary Ocean Color Imager data. International Journal of Remote Sensing, 39(6), 1929-1949. https://doi.org/10.1080/01431161.2017.1416699
  71. Pitarch, J., Volpe, G., Colella, S., Krasemann, H., and Santoleri, R., 2016, Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data. Ocean Science, 12(2), 379-389. https://doi.org/10.5194/os-12-379-2016
  72. Pottier, C., Garçon, V., Larnicol, G., Sudre, J., Schaeffer, P. and Le Traon, P.-Y., 2006, Merging SeaWiFS and MODIS/Aqua ocean color data in North and Equatorial Atlantic using weighted averaging and objective analysis. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3436-3451. https://doi.org/10.1109/TGRS.2006.878441
  73. Pradhan, Y., Thomaskutty, A.V., Rajawat, A.S., and Nayak, S., 2005, Improved regional algorithm to retrieve total suspended particulate matter using IRS-P4 ocean colour monitor data. Journal of Optics A: Pure and Applied Optics, 7(7), 343. https://doi.org/10.1088/1464-4258/7/7/012
  74. Racault, M.F., Le Quéré, C., Buitenhuis, E., Sathyendranath, S., and Platt, T., 2012, Phytoplankton phenology in the global ocean. Ecological Indicators, 14(1), 152-163. https://doi.org/10.1016/j.ecolind.2011.07.010
  75. Rast, M., Bezy, J.L., and Bruzzi, S., 1999, The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission. International Journal of Remote Sensing, 20(9), 1681-1702. https://doi.org/10.1080/014311699212416
  76. Robinson, I.S., 2004, Measuring the oceans from space: The principles and methods of satellite oceanography. Springer Science & Business Media, Chichester, UK, 670 p.
  77. Roesler, C.S. and Perry, M.J., 1995, In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance. Journal of Geophysical Research: Oceans, 100(C7), 13279-13294. https://doi.org/10.1029/95JC00455
  78. Ruddick, K.G., Gons, H.J., Rijkeboer, M., and Tilstone, G., 2001, Optical remote sensing of chlorophyll a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties. Applied Optics, 40(21), 3575-3585. https://doi.org/10.1364/AO.40.003575
  79. Sathyendranath, S. and Platt, T., 1997, Analytic model of ocean color. Applied Optics, 36(12), 2620-2629. https://doi.org/10.1364/AO.36.002620
  80. Sathyendranath, S., Prieur, L., and Morel, A., 1989, A three-component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters. International Journal of Remote Sensing, 10(8), 1373-1394. https://doi.org/10.1080/01431168908903974
  81. Schiller, H. and Doerffer, R., 2005, Improved determination of coastal water constituent concentrations from MERIS data. IEEE Transactions on Geoscience and Remote Sensing, 43(7), 1585-1591. https://doi.org/10.1109/TGRS.2005.848410
  82. Seegers, B.N., Stumpf, R.P., Schaeffer, B.A., Loftin, K.A., and Werdell, P.J., 2018, Performance metrics for the assessment of satellite data products: An ocean color case study. Optics Express, 26, 7404-7422. https://doi.org/10.1364/OE.26.007404
  83. Shen, L., Xu, H., and 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
  84. Siegel, D.A., Behrenfeld, M.J., Maritorena, S., McClain, C.R., Antoine, D., Bailey, S.W., ... and Eplee Jr, R.E., 2013, Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission. Remote Sensing of Environment, 135, 77-91. https://doi.org/10.1016/j.rse.2013.03.025
  85. Siswanto, E., Tang, J., Yamaguchi, H., Ahn, Y. H., Ishizaka, J., Yoo, S., ... and Kawamura, H., 2011, Empirical ocean-color algorithms to retrieve chlorophylla, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas. Journal of Oceanography, 67(5), 627-650. https://doi.org/10.1007/s10872-011-0062-z
  86. Smetacek, V. and Cloern, J.E., 2008, On phytoplankton trends. Science, 319(5868), 1346-1348. https://doi.org/10.1126/science.1151330
  87. Strickland, J.D. and Parsons, T.R., 1972, A practical handbook of seawater analysis. Fisheries Research Board of Canada 167, Ottawa, Canada, 310 p.
  88. Tassan, S., 1994, Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. Applied Optics, 33(12), 2369-2378. https://doi.org/10.1364/AO.33.002369
  89. Tilstone, G.H., Lotliker, A.A., Miller, P.I., Ashraf, P.M., Kumar, T.S., Suresh, T., ... and Menon, H.B., 2013, Assessment of MODIS-Aqua chlorophyll-a algorithms in coastal and shelf waters of the eastern Arabian Sea. Continental Shelf Research, 65, 14-26. https://doi.org/10.1016/j.csr.2013.06.003
  90. Urbanski, J.A., Wochna, A., Bubak, I., Grzybowski, W., Lukawska-Matuszewska, K., Lacka, M., Sliwinska, S., Wojtasiewicz, B., and Zajaczkowski, M., 2016, Application of Landsat 8 imagery to regional-scale assessment of lake water quality. International Journal of Applied Earth Observation and Geoinformation, 51, 28-36. https://doi.org/10.1016/j.jag.2016.04.004
  91. Uz, M. and Yoder, J.A., 2004, High frequency and mesoscale variability in SeaWiFS chlorophyll imagery and its relation to other remotely sensed oceanographic variables. Deep-Sea Research II, 51, 1001-1017. https://doi.org/10.1016/S0967-0645(04)00097-9
  92. Werdell, P.J. and Bailey, S.W., 2005, An improved biooptical data set for ocean color algorithm development and satellite data product validation. Remote Sensing of Environment, 98(1), 122-140. https://doi.org/10.1016/j.rse.2005.07.001
  93. Wilson, C. and Adamec, D., 2002, A global view of biophysical coupling from SeaWiFS and TOPEX satellite data, 1997-2001. Geophysical Research Letters, 29(8), 1257.
  94. Wilson, C. and Coles, V.J., 2005, Global climatological relationships between satellite biological and physical observations and upper ocean properties. Journal of Geophysical Research: Oceans, 110(C10).
  95. Wilson, C., 2011, The rocky road from research to operations for satellite ocean colour data in fishery management. ICES Journal of Marine Science, 68, 677-686. https://doi.org/10.1093/icesjms/fsq168
  96. Yentsch, C.S. and Menzel, D.W., 1963, A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. Deep Sea Research and Oceanographic Abstracts, 10(3), 221-231. https://doi.org/10.1016/0011-7471(63)90358-9
  97. Yoder, J.A. and Kennelly, M.A., 2003, Seasonal and ENSO variability in global ocean phytoplankton chlorophyll derived from 4 years of SeaWiFS measurements. Global Biogeochemical Cycles, 17(4), 1112.
  98. Yoder, J.A., Esaias, W.E., Feldman, G.C., and McClain, C.R., 1988, Satellite ocean color-status report. Oceanography, 1(1), 18-20. https://doi.org/10.5670/oceanog.1988.34
  99. Yoon, J.-E., Lim, J.-H., Son, S, Youn, S.-H., Oh, H.-J., Hwang, J.-D., Kwon, J.-I., Kim, S.-S., and Kim, I.-N., 2019, Assessment of satellite-based chlorophyll-a algorithms in eutrophic Korean coastal waters: Jinhae Bay case study. Frontiers in Marine Science, 6, 359. https://doi.org/10.3389/fmars.2019.00359