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Overview and Prospective of Satellite Chlorophyll-a Concentration Retrieval Algorithms Suitable for Coastal Turbid Sea Waters

연안 혼탁 해수에 적합한 위성 클로로필-a 농도 산출 알고리즘 개관과 전망

  • Park, Ji-Eun (Center of Remote Sensing and GIS, Korea Polar Research Institute) ;
  • Park, Kyung-Ae (Department of Earth Science Education/Research Institute of Oceanography, Seoul National University) ;
  • Lee, Ji-Hyun (Department of Science Education, Seoul National University)
  • 박지은 (극지연구소 원격탐사빙권정보센터) ;
  • 박경애 (서울대학교 지구과학교육과/해양연구소) ;
  • 이지현 (서울대학교 과학교육과)
  • Received : 2021.06.16
  • Accepted : 2021.06.24
  • Published : 2021.06.30

Abstract

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

최근의 기후변화는 연안에서 더욱 가속화되고 있어 연안에서의 해양 환경변화 감시의 중요성이 커지고 있다. 클로로필-a 농도는 해양 환경 변화의 중요한 지표 중 하나로 수십년 동안 여러 해색 위성을 통해 전구 해양 표층의 클로로필-a 농도가 산출되었으며 다양한 연구 분야에 활용되었다. 하지만 연안 해역의 탁한 해수는 외해의 맑은 해수와는 구별되는 구성 성분과 광학적 특성으로 인해 나타나는 심각한 오차 때문에 일반적으로 사용되는 전지구 대양을 위하여 만들어진 클로로필-a 농도 알고리즘은 연안 해역에 대입할 수 없다. 또한 연안 해역은 해역에 따라 성분과 특성이 크게 달라져 통일된 하나의 알고리즘을 제시하기 어렵다. 이러한 문제점을 극복하기 위하여 연안의 탁도가 높은 해역에서는 구성 성분과 광학적 변동 특성을 고려한 다양한 알고리즘들이 개발되어 사용되어 왔다. 클로로필-a 농도 산출 알고리즘은 크게 경험적 알고리즘, 반해석적 알고리즘, 기계학습을 활용한 알고리즘 등으로 나눌 수 있다. 해수의 반사 스펙트럼에 기반한 청색-녹색 밴드 비율이 기본적인 형태로 주로 사용된다. 반면 탁한 해수를 위해 개발된 알고리즘은 연안해역에 존재하는 용존 유기물과 부유물의 영향을 상쇄시키기 위한 방식으로 녹색-적색 밴드 비율, 적색-근적외 밴드 비율, 고유한 광학적 특성 등을 사용한다. 탁한 해수에서의 신뢰성 있는 위성 클로로필-a 농도 산출은 미래의 연안 해역을 관리하고 연안 생태 변화를 감시하는데 필수적이다. 따라서 본 연구는 탁도가 높은 Case 2 해수에서 활용되어온 알고리즘들을 요약하고, 한반도 주변해역의 모니터링과 연구에 대한 문제점을 제시한다. 또한 다분광 및 초분광 센서의 개발로 더욱 정확하고 다양한 해색 환경을 이해할 수 있는 미래의 해색 위성에 대한 발전 전망도 제시한다.

Keywords

Acknowledgement

이 연구는 해양수산부 재원으로 한국해양과학기술원 '다종위성 기반 해양 현안대응 실용화 기술 개발'의 지원을 받아 수행되었습니다. 해양수산부 국립해양조사원 연구사업(이어도 해양과학기지 활용 황·동 중국해 중장기 해양환경 변화 연구)의 일부 지원을 받아 수행되었습니다.

References

  1. Abbas, M. M., Melesse, A. M., Scinto, L. J., and Rehage, J. S., 2019, Satellite Estimation of Chlorophyll-a Using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in Shallow Coastal Water Bodies: Validation and Improvement. Water, 11(8), 1621. https://doi.org/10.3390/w11081621
  2. Aiken, J., Moore, G. F., Trees, C. C., Hooker, S. B., and Clark, D. K., 1996, The SeaWiFS CZCS-type pigment algorithm. Oceanographic Literature Review, 3(43), 315-316.
  3. Babin, M. and Stramski, D., 2004, Variations in the massspecific absorption coefficient of mineral particles suspended in water. Limnology and Oceanography, 49(3), 756-767. https://doi.org/10.4319/lo.2004.49.3.0756
  4. Bissett, W. P., Schofield, O., Glenn, S., Cullen, J. J., Miller, W. L., Plueddemann, A. J., and Mobley, C. D., 2001, Resolving the impacts and feedbacks of ocean optics on upper ocean ecology. Oceanography, 14(3), 30-53. https://doi.org/10.5670/oceanog.2001.22
  5. 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
  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. Boyce, D. G., Lewis, M. R., and Worm, B., 2010, Global phytoplankton decline over the past century. Nature, 466(7306), 591-596. https://doi.org/10.1038/nature09268
  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. Cao, Z., Ma, R., Duan, H., Pahlevan, N., Melack, J., Shen, M., and Xue, K., 2020, A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sensing of Environment, 248, 111974. https://doi.org/10.1016/j.rse.2020.111974
  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 nitrate-depletion temperatures. Journal of Geophysical Research: Oceans, 104(C3), 5403-5421. https://doi.org/10.1029/1998JC900082
  11. 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
  12. Claustre, H., Babin, M., Merien, D., Ras, J., Prieur, L., Dallot, S., Prasil, O., Dousova, H., and Moutin, T., 2005, Toward a taxon-specific parameterization of biooptical models of primary production: A case study in the North Atlantic. Journal of Geophysical Research: Oceans, 110(C7).
  13. Cui, T., Zhang, J., Groom, S., Sun, L., Smyth, T., Sathyendranath, S., 2010, Validation of MERIS oceancolor products in the Bohai Sea: A case study for turbid coastal waters. Remote Sensing Environment, 114, 2326-2336. https://doi.org/10.1016/j.rse.2010.05.009
  14. Cullen, J. J., 1982, The deep chlorophyll maximum: comparing vertical profiles of chlorophyll a. Canadian Journal of Fisheries and Aquatic Sciences, 39(5), 791-803. https://doi.org/10.1139/f82-108
  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. https://doi.org/10.1073/pnas.0913800107
  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. Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M. H., Femenias, P., Frerick, J., Goryl, P., Klein, U., Laur, H., Mavrocordatos, C., Nieke, J., Rebhan, H., Seitz, B., Stroede, J., and Sciarra, R., 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
  18. 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
  19. 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
  20. 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
  21. Gitelson, A. A., Schalles, J. F., Rundquist, D. C., Schiebe, F. R., and Yacobi, Y. Z., 1999, Comparative reflectance properties of algal cultures with manipulated densities. Journal of Applied Phycology, 11(4), 345-354. https://doi.org/10.1023/A:1008143902418
  22. 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.
  23. Gower, J., 2000, Productivity and plankton blooms observed with SeaWiFS. In. Proc. 5th Pacific Ocean Remote Sensing Conference, PORSEC, 23-27.
  24. 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
  25. Gower, J., King, S., Borstad, G., and Brown, L., 2005. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 26, 2005-2012. https://doi.org/10.1080/01431160500075857
  26. Groom, S., Sathyendranath, S., Ban, Y., Bernard, S., Brewin, R., Brotas, V., Brockmann, C., Chauhan, P., Choi, J., Chuprin, A., Ciavatta, S., Cipollini, P., Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T., Kampel, M., Krasemann, H., Lavender, S., PardoMartinez, S., Melin, F., Platt, T., Santoleri, R., Skakala, J., Schaeffer, B., Smith, M., Steinmetz, F., Valente, A., and Wang, M., 2019, Satellite ocean colour: current status and future perspective. Frontiers in Marine Science, 6, 485. https://doi.org/10.3389/fmars.2019.00485
  27. Gross, L., Thiria, S., Frouin, R., and Mitchell, B. G., 2000, Artificial neural networks for modelling the transfer function between marine reflectance and phytoplankton pigment concentration. Journal of Geophysical Research, 105, 3483-3495. https://doi.org/10.1029/1999jc900278
  28. Gurlin, D., Gitelson, A. A., and 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(12), 3479-3490. https://doi.org/10.1016/j.rse.2011.08.011
  29. Hastie, T. and Tibshirani, R., 1990, Exploring the nature of covariate effects in the proportional hazards model. Biometrics, 1005-1016.
  30. Hattab, T., Jamet, C., Sammari, C., and Lahbib, S., 2013, Validation of chlorophyll-α 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
  31. He, M. X., Liu, Z. S., Du, K. P., Li, L. P., Chen, R., Carder, K. L., and Lee, Z. P., 2000, Retrieval of chlorophyll from remote-sensing reflectance in the China seas. Applied Optics, 39(15), 2467-2474. https://doi.org/10.1364/AO.39.002467
  32. Hieronymi, M., Muller, D., and Doerffer, R., 2017, The OLCI Neural Network Swarm (ONNS): a bio-geooptical algorithm for open ocean and coastal waters. Frontiers in Marine Science, 4, 140. https://doi.org/10.3389/fmars.2017.00140
  33. Hojerslev, N. K., 1980, Water color and its relation to primary production. Boundary-Layer Meteorology, 18(2), 203-220. https://doi.org/10.1007/BF00121324
  34. 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.
  35. Hovis, W. A., 1981, The Nimbus-7 coastal zone color scanner (CZCS) program. In Oceanography from space. Springer, MA, USA, 213-225.
  36. 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
  37. 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).
  38. 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
  39. Ioannou I., Gilerson, A., Gross, B., Moshary, F., and Ahmed, S., 2011, Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS, Applied Optics, 50(19), 3168-3186. https://doi.org/10.1364/AO.50.003168
  40. IOCCG, 2000, Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex, Waters, In Sathyendranath, S. (ed.), Reports of the International Ocean-Colour Coordinating Group No. 3. Dartmouth, NS, Canada, 140 p.
  41. 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, NS, Canada, 126 p.
  42. IOCCG, 2012a, 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.
  43. IOCCG, 2012b, Mission requirements for future oceancolour sensors. In McClain, C. and Meister, G. (ed.), Reports of the International Ocean Colour Coordinating Group. NASA Goddard Space Flight Center, Greenbelt (MD, USA), 106 p.
  44. Irwin, A. J. and Finkel, Z. V., 2008, Mining a sea of data: Deducing the environmental controls of ocean chlorophyll. PloS One, 3(11), e3836. https://doi.org/10.1371/journal.pone.0003836
  45. Jamet, C., Loisel, H., and Dessailly, D., 2012, Retrieval of the spectral diffuse attenuation coefficient Kd (λ) in open and coastal ocean waters using a neural network inversion. Journal of Geophysical Research: Oceans, 117(C10).
  46. Joint, I. I. and Groom, S. B., 2000, Estimation of phytoplankton production from space: current status and future potential of satellite remote sensing. Journal of Experimental Marine Biology and Ecology, 250, 233-255. https://doi.org/10.1016/S0022-0981(00)00199-4
  47. Kajiyama, T., D'Alimonte, D., and Zibordi, G., 2018, Algorithms Merging for the Determination of Chlorophyll-a Concentration in the Black Sea. IEEE Geoscience and Remote Sensing Letters, 16(5), 677-681. https://doi.org/10.1109/lgrs.2018.2883539
  48. 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
  49. 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
  50. Le, C., Hu, C., Cannizzaro, J., and Duan, H., 2013a, Longterm distribution patterns of remotely sensed water quality parameters in Chesapeake Bay. Estuarine, Coastal and Shelf Science, 128, 93-103. https://doi.org/10.1016/j.ecss.2013.05.004
  51. Le, C., Hu, C., Cannizzaro, J., English, D., Muller-Karger, F., and Lee, Z., 2013b, Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary. Remote Sensing of Environment, 129, 75-89. https://doi.org/10.1016/j.rse.2012.11.001
  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. 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
  55. Martin, A. P., 2003, Phytoplankton patchiness: the role of lateral stirring and mixing. Progress in Oceanography, 57(2), 125-174. https://doi.org/10.1016/S0079-6611(03)00085-5
  56. 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
  57. McKinna, L. I., Fearns, P. R., Weeks, S. J., Werdell, P. J., Reichstetter, M., Franz, B. A., Shea, D. M., and Feldman, G. C., 2015, A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization. Journal of Geophysical Research: Oceans, 120(3), 1741-1770. https://doi.org/10.1002/2014JC010224
  58. Mobley, C. D. and Stramski, D., 1994, Influences of microbial particles on oceanic optics. In Ocean Optics XII (Vol. 2258, pp. 184-193). International Society for Optics and Photonics.
  59. Mobley, C. D., Stramski, D., Paul Bissett, W., and Boss, E., 2004, Optical modeling of ocean waters: Is the case 1-case 2 classification still useful?. Oceanography, 17(2), 60-67. https://doi.org/10.5670/oceanog.2004.48
  60. Moon, J. E., Ahn, Y. H., Ryu, J. H., and Shanmugam, P., 2010, Development of ocean environmental algorithms for Geostationary Ocean Color Imager (GOCI). Korean Journal of Remote Sensing, 26(2), 189-207. https://doi.org/10.7780/KJRS.2010.26.2.189
  61. Moore, T. S., Campbell, J. W., and Dowell, M. D., 2009, A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sensing of Environment, 113(11), 2424-2430. https://doi.org/10.1016/j.rse.2009.07.016
  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. Morel, A., 1988, Optical modeling of the upper ocean in relation to its biogenous matter content (case I waters). Journal of Geophysical Research: Oceans, 93(C9), 10749-10768. https://doi.org/10.1029/JC093iC09p10749
  64. Moses, W. J., Gitelson, A. A., Berdnikov, S., Saprygin, V., and Povazhnyi, V., 2012, Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters-The Azov Sea case study. Remote Sensing of Environment, 121, 118-124. https://doi.org/10.1016/j.rse.2012.01.024
  65. Murakami, H., 2016, Ocean color estimation by Himawari8/AHI. In Proceedings of SPIE Asia-Pacific Remote Sensing. International Society for Optics and Photonics, 2016, New Delhi, India, 987810.
  66. Neville, R. A. and Gower, J. F. R., 1977, Passive remote sensing of phytoplankton via chlorophyll α fluorescence. Journal of Geophysical Research, 82(24), 3487-3493. https://doi.org/10.1029/JC082i024p03487
  67. Odermatt, D., Gitelson, A., Brando, V. E., and Schaepman, M., 2012, Review of constituent retrieval in optically deep and complex waters from satellite imagery. Remote sensing of environment, 118, 116-126. https://doi.org/10.1016/j.rse.2011.11.013
  68. 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
  69. 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
  70. Pahlevan, N., Smith, B., Schalles, J., Binding, C., Cao, Z., Ma, R., Alikas, K., Kangro, K., Gurlin, D., Ha, N., Matsushita, B., Moses, W., Greb, S., Lehmann, M. K., Ondrusek, M., Oppelt, N., and Stumpf, R., 2020, Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604. https://doi.org/10.1016/j.rse.2019.111604
  71. 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
  72. 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
  73. Pozdnyakov, D., Lyaskovsky, A., Grassl, H., and Pettersson, L., 2002, Numerical modelling of transspectral processes in natural waters: implications for remote sensing. International Journal of Remote Sensing, 23(8), 1581-1607. https://doi.org/10.1080/014311601170735
  74. 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
  75. Raitsos, D. E., Korres, G., Triantafyllou, G., Petihakis, G., Pantazi, M., Tsiaras, K., and Pollani, A., 2012, Assessing chlorophyll variability in relation to the environmental regime in Pagasitikos Gulf, Greece. Journal of Marine Systems, 94, S16-S22.
  76. 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
  77. Robinson, I. S., 2004, Measuring the oceans from space: The principles and methods of satellite oceanography. Springer Science & Business Media, Chichester, UK, 670 p.
  78. 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
  79. 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
  80. Schalles, J. F., 2006, Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal. In Remote sensing of aquatic coastal ecosystem processes, Springer, Dordrecht, Netherlands, 27-79.
  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. Sen Gupta, A., McNeil, B., 2012. Variability and change in the ocean. In: Henderson-Sellers, A., McGuffie, K. (Eds.), The Future of the World's Climate, second ed. Elsevier, Boston, 141-165.
  83. Shang, S., Dong, Q., Lee, Z., Li, Y., Xie, Y., and Behrenfeld, M., 2011, MODIS observed phytoplankton dynamics in the Taiwan Strait: an absorption-based analysis. Biogeosciences, 8(4), 841-850. https://doi.org/10.5194/bg-8-841-2011
  84. Shanmugam, P., 2011. A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters. Journal of Geophysical Research: Oceans, 116, 12.
  85. Shin, J., Kim, K., and Ryu, J. -H., 2020, Comparative Study on Hyperspectral and Satellite Image for the Estimation of Chlorophyll a Concentration on Coastal Areas. Korean Journal of Remote Sensing, 36(2-2), 309-323.
  86. Siegel, H., Ohde, T., Gerth, M., Lavik, G., and Leipe, T., 2007, Identification of coccolithophore blooms in the SE Atlantic Ocean off Namibia by satellites and in-situ methods. Continental Shelf Research, 27(2), 258-274. https://doi.org/10.1016/j.csr.2006.10.003
  87. Siswanto, E., Tang, J., Yamaguchi, H., Ahn, Y. H., Ishizaka, J., Yoo, S., Kim, S. W., Kiyomoto, Y., Yamada, K., Chiang, C., 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
  88. Smith, B., Pahlevan, N., Schalles, J., Ruberg, S., Errera, R., Ma, R., Giardino C., Bresciani M., Barbosa C., Moore T., Fernandez V., Alikas K., and Kangro K., 2021, A chlorophyll-a algorithm for Landsat-8 based on mixture density networks. Frontiers in Remote Sensing, 1, 5.
  89. Stock, A., 2015, Satellite mapping of Baltic Sea Secchi depth with multiple regression models. International Journal of Applied Earth Observation and Geoinformation, 40, 55-64. https://doi.org/10.1016/j.jag.2015.04.002
  90. Strickland, J. D. and Parsons, T. R., 1972, A practical handbook of seawater analysis. Fisheries Research Board of Canada 167, Ottawa, Canada, 310 p.
  91. 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
  92. Tilstone, G. H., Lotliker, A. A., Miller, P. I., Ashraf, P. M., Kumar, T. S., Suresh, T., Ragavan, B. R., 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
  93. Tzortziou, M., Subramaniam, A., Herman, J. R., Gallegos, C. L., Neale, P. J., and Harding Jr, L. W., 2007, Remote sensing reflectance and inherent optical properties in the mid Chesapeake Bay. Estuarine, Coastal and Shelf Science, 72(1-2), 16-32. https://doi.org/10.1016/j.ecss.2006.09.018
  94. Wang, Y., Liu, D., and Tang, D., 2017, Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas, China. International Journal of Remote Sensing, 38(3), 639-661. https://doi.org/10.1080/01431161.2016.1268733
  95. Wei, J., Lee, Z., and Shang, S., 2016, A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments. Journal of Geophysical Research: Oceans, 121(11), 8189-8207. https://doi.org/10.1002/2016JC012126
  96. Werdell, P. J., McKinna, L. I., Boss, E., Ackleson, S. G., Craig, S. E., Gregg, W. W., Lee, Z., Maritorena, S., Roesler, C. S., Rousseaux, C. S., Stramski, D., Sullivan, J. M., Twardowskik, M. S., Tzortziou, M., and Zhang, X., 2018, An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Progress in oceanography, 160, 186-212. https://doi.org/10.1016/j.pocean.2018.01.001
  97. Yang, M. M., Ishizaka, J., Goes, J. I., Gomes, H. D. R., Maure, E. D. R., Hayashi, M., Katano, T., Fujii, N., Saitoh, K., Mine, T., Yamashita, H., Fujii, N., and Mizuno, A., 2018, Improved MODIS-Aqua chlorophyll-a retrievals in the turbid semi-enclosed Ariake Bay, Japan. Remote Sensing, 10(9), 1335. https://doi.org/10.3390/rs10091335
  98. 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
  99. 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.
  100. Zhan, H., Shi, P., and Chen, C., 2003, Retrieval of oceanic chlorophyll concentration using support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 41(12), 2947-2951. https://doi.org/10.1109/TGRS.2003.819870