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http://dx.doi.org/10.5467/JKESS.2021.42.3.247

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

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)
Publication Information
Journal of the Korean earth science society / v.42, no.3, 2021 , pp. 247-263 More about this Journal
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.
Keywords
Chlorophyll-a concentration; algorithm; turbid sea water; ocean color; coastal region;
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1 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.
2 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.
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 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.
8 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.   DOI
9 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.   DOI
10 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.   DOI
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 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.   DOI
15 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.
16 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.   DOI
17 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.   DOI
18 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.   DOI
19 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.   DOI
20 Gower, J., 2000, Productivity and plankton blooms observed with SeaWiFS. In. Proc. 5th Pacific Ocean Remote Sensing Conference, PORSEC, 23-27.
21 Strickland, J. D. and Parsons, T. R., 1972, A practical handbook of seawater analysis. Fisheries Research Board of Canada 167, Ottawa, Canada, 310 p.
22 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).
23 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.   DOI
24 Doerffer, R. and Schiller, H., 2007, The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28(3-4), 517-535.   DOI
25 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.   DOI
26 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).
27 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.   DOI
28 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.   DOI
29 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.
30 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.   DOI
31 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.   DOI
32 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.   DOI
33 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.   DOI
34 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.   DOI
35 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.   DOI
36 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.   DOI
37 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.
38 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.   DOI
39 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.   DOI
40 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.
41 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.   DOI
42 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.   DOI
43 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.   DOI
44 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.   DOI
45 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.
46 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.   DOI
47 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.   DOI
48 Robinson, I. S., 2004, Measuring the oceans from space: The principles and methods of satellite oceanography. Springer Science & Business Media, Chichester, UK, 670 p.
49 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.   DOI
50 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.   DOI
51 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.   DOI
52 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.   DOI
53 Hastie, T. and Tibshirani, R., 1990, Exploring the nature of covariate effects in the proportional hazards model. Biometrics, 1005-1016.
54 Martin, A. P., 2003, Phytoplankton patchiness: the role of lateral stirring and mixing. Progress in Oceanography, 57(2), 125-174.   DOI
55 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.   DOI
56 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.   DOI
57 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.   DOI
58 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.   DOI
59 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.   DOI
60 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.   DOI
61 McClain, C. R., 2009, A decade of satellite ocean color observations. Annual Review of Marine Science, 1, 19-42.   DOI
62 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.   DOI
63 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.
64 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.   DOI
65 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.   DOI
66 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.
67 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.   DOI
68 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.   DOI
69 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.
70 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.   DOI
71 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.   DOI
72 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.   DOI
73 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.   DOI
74 Morel, A. and Prieur, L., 1977, Analysis of variations in ocean color 1. Limnology and Oceanography, 22(4), 709-722.   DOI
75 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.   DOI
76 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.   DOI
77 Boyce, D. G., Lewis, M. R., and Worm, B., 2010, Global phytoplankton decline over the past century. Nature, 466(7306), 591-596.   DOI
78 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.
79 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.   DOI
80 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.   DOI
81 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.   DOI
82 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.   DOI
83 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.   DOI
84 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.   DOI
85 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.   DOI
86 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.   DOI
87 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.   DOI
88 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.
89 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.
90 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.
91 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.   DOI
92 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.
93 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.   DOI
94 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.   DOI
95 Hojerslev, N. K., 1980, Water color and its relation to primary production. Boundary-Layer Meteorology, 18(2), 203-220.   DOI
96 Hovis, W. A., 1981, The Nimbus-7 coastal zone color scanner (CZCS) program. In Oceanography from space. Springer, MA, USA, 213-225.
97 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.   DOI
98 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.   DOI
99 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).
100 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.