The IOCCG bibliography is updated periodically when new references are submitted by readers. Another useful ocean colour bibliography is the searchable Historic Ocean Colour Archive assembled by Marcel Wernand, with articles and books written between the 17th and early 20th century.
If you would like to submit a peer-reviewed publication to be included in the IOCCG Bibliography, please send the reference to Raisha Lovindeer using the following format: Lastname1, Initials1., Lastname2, Initials2., etc. (DATE). Full title of publication, Journal Abbreviation, Volume, Page numbers, DOI (if available). Please also check to see if the reference is not already in the database (search by first author). It is not necessary to send the PDF file as an attachment. Note that only peer-reviewed articles will be accepted.
If you would like to view recently-published papers, enter the current year in “Search by Keyword”. You can also search the database using keywords or the author’s last name. For papers dealing with Remote Sensing of Marine Litter and Debris, use the keyword “RSMLD”. You can also view the Datasets Bibliography for remote sensing and marine litter and debris.
Bibliography
Arrigo, K.R. and Sullivan, C.W. (1994). A high resolution bio-optical model of microalgal growth: Tests using sea-ice algal community time-series data. Limnol. Oceanogr., 39: 609-631.
Arrigo, K.R. and van Dijken, G.L. (2004). Annual changes in sea-ice, chlorophyll a, and primary production in the Ross Sea, Antarctica. Deep Sea Research. II. Topical Studies in Oceanography.51: 117-138.
Arrigo, K.R., Robinson, D.H. and Sullivan, C.W. (1993). A high resolution study of the platelet ice ecosystem in McMurdo Sound, Antarctica: photosynthetic and bio-optical characteristics of a dense microalgal bloom. Mar. Ecol. Prog. Ser., 98: 173-185.
Arrigo, K.R., Robinson, D.H., Worthen, D.L., Schieber, B. and Lizotte, M.P. (1998). Bio-optical properties of the southwestern Ross Sea. J. Geophys. Res., 103: 21,683-21,695.
Arrigo, K.R., Worthen, D.L., Robinson, D.H. (2003). A coupled ocean-ecosystem model of the Ross Sea: 2. Iron regulation of phytoplankton taxonomic variability and primary production. J. Geophys. Res., 108(C7): 3231 10.1029/2001JC000856, 16 July 2003[HTML].
Arst, H. A. Erm, A. Herlevi, T. Kutser, M. Leppäranta, A. Reinart, J. Virta (2008). Optical properties of boreal lake waters in Finland and Estonia. Boreal Environment Research, 13: 133-158.
Arteaga, L.A., Rousseaux, C.S. (2023) Impact of Pacific Ocean heatwaves on phytoplankton community composition. Commun Biol 6, 263. https://doi.org/10.1038/s42003-023-04645-0
Arvesen, J.C., Millard, J.P. and Weaver, E.C. (1973). Remote sensing of chlorophyll and temperature in marine and fresh waters. Astronaut. A., 18: 229-239.
Asamoah, B.O., Uurasjärvi, E., Räty, J., Koistinen, A., Roussey, M., Peiponen, K.-E. (2021). Towards the development of portable and in situ optical devices for detection of micro-and nanoplastics in water: A review on the current status. Polymers, 13, 730. https://doi.org/10.3390/polym13050730
Asrar, G., Kaye, J. A., Morel, P. (2001) NASA Research Strategy for Earth System Science: Climate Component. Bull. Amer. Meteor. Soc., 82(7), 1,309-1,329.
Astorayme, M. A., Vázquez-Rowe, I., and Kahhat, R. (2024) The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review. Sci. Total Environ., vol 945, pp 173843, https://doi.org/10.1016/j.scitotenv.2024.173843.
Astoreca R, Rousseau V, Ruddick K, Knechciak C, Van Mol B, Parent J-Y and Lancelot, C. (2009). Development and application of an algorithm for detecting Phaeocystis globosa blooms in the Case 2 Southern North Sea waters. Journal of Plankton Research31(3): 287-300. [PDF File]
Astoreca R, V Rousseau and C Lancelot. 2009. Coloured dissolved organic matter (CDOM) in Southern North Sea waters: Optical characterization and possible origin. Estuarine, Coastal and Shelf Science, 85:633-640. [PDF File]
Atlas, T. and Bannister, T.T. (1980). Dependence of mean spectral extinction coefficient of phytoplankton on depth, water colour, and species. Limnol. Oceanogr., 19: 1-12.
Atwood, E.C., Falcieri, F.M., Piehl, S., Bochow, M. Matthies, M., Franke, J., Carniel, S., Sclavo, M., Laforsch, C., Siegert, F. (2019). Coastal accumulation of microplastic particles emitted from the Po River, Northern Italy: Comparing remote sensing and hydrodynamic modelling with in situ sample collections. Marine Pollution Bulletin, 138, 561-574. https://doi.org/10.1016/j.marpolbul.2018.11.045
Aurin, D. A. and Dierssen, H. M. (2012). Advantages and limitations of ocean color remote sensing in CDOM-dominated, mineral-rich coastal and estuarine waters. Remote Sens. Environ. , 125: 181-194 [Full article]
Aurin, D. A., H. M. Dierssen, M. S. Twardowski and C. S. Roesler (2010). Optical complexity in Long Island Sound and implications for coastal ocean color remote sensing. J. Geophys. Res., 111: (C07011) [Abstract]
Austin, R.W. (1974). The remote sensing of spectral radiance from below the ocean surface. In: Optical Aspects of Oceanography. N.G. Jerlov and E. Steemann-Nielsen (Eds.), Academic Press, London, New York, 317-344.
Austin, R.W. (1979). Coastal zone color scanner radiometry. Proc. SPIE, 208: 170-177.
Austin, R.W. (1980). Gulf of Mexico, ocean-color surface-truth measurements. Boundary-Layer Meteorol., 18: 269-285.
Austin, R.W. and Petzold, T.J. (1981). The determination of the diffuse attenuation coefficient of sea water using the coastal zone color scanner. In: Oceanography from space, J.F.R. Gower (Ed.). Plenum Press, New York, 239-256.
Avouris, D.M. and Joseph D. Ortiz (2019). Validation of 2015 Lake Erie MODIS image spectral decomposition using visible derivative spectroscopy and field campaign data. Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.02.005.
Babichenko, S., Leeben, A., Poryvkina, L., Shalapyonok, A. and Seppala, J. (2001). Variability of Chlorella sp. fluorescence in response to different nitrogen conditions, Int. J. Rem. Sens., 22(2&3): 403-414.
Babin, M., and Stramski, D. (2002). Light absorption by aquatic particles in the near-infrared spectral region. Limnol. Oceanogr.,47: 911-915.
Babin, M., Morel, A. and Gagnon, R. (1994). An incubator designed for extensive and sensitive measurements of phytoplankton photosynthetic parameters. Limnol. Oceanogr., 39: 496-510.
Babin, M., Morel, A. and Gentili, B. (1996). Remote sensing of surface Sun-induced chlorophyll fluorescence: consequences of natural variations in the optical characteristics of phytoplankton and the quantum yield of chlorophyll a fluorescence. Int. J. Rem. Sens., 17: 2417-2448.
Babin, M., Morel, A., Claustre, H., Bricaud, A., Kolber, Z. and Falkowski P.G. (1996). Nitrogen-and irradiance-dependent variations of the maximum quantum yield of carbon fixation in eutrophic, mesotrophic and oligotrophic marine systems. Deep-Sea Res., 43(8): 1241-1272.
Babin, M., Stramski, D., Ferrari, G.M., Claustre, H., Bricaud, A., Obolensky, G., Hoepffner, N. (2003). Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. J. Geophys. Res., 108(C7): 3211 10.1029/2001JC000882.
Babin, M., Therriault, J.-C. and Legendre, L. (1991). Potential utilization of temperature in estimating primary production from remote sensing data in coastal and estuarine waters. Estuar. Coast. Shelf Sci., 33: 559-579.
Babin, M., Therriault, J.C., Legendre, L., Nieke, B., Reuter, R. and Condal, A. (1995). Relationship between the maximum quantum yield of carbon fixation and the minimum quantum yield of chlorophyll a in vivo fluorescence in the Gulf of St. Lawrence. Limnol. Oceanogr., 40, 956-968.
Babin, S. M.; Carton, J. A.; Dickey, T. D.; Wiggert, J. D.(2004). Satellite evidence of hurricane-induced phytoplankton blooms in an oceanic desert J. Geophys. Res., Vol. 109, No. C3, C03043 10.1029/2003JC001938 25 March 2004
Bagheri, S., Zetlin, C. and Dios, R. (1999). Estimation of optical properties of nearshore water. Int. J. Rem. Sens., 20(17): 3393-3396.
Bai, Y., D. Pan, W.-J. Cai, X. He, D. Wang, B. Tao, and Q. Zhu (2013). Remote sensing of salinity from satellite-derived CDOM in the Changjiang River dominated East China Sea, Journal of Geophysical Research: Oceans, 118, 227–243, doi:10.1029/2012JC008467 [Full article].
Bai, Y., W.-J. Cai, X. He, W Zhai, D. Pan, M. Dai, and P. Yu (2015). A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: A case study from the East China Sea. Journal of Geophysical Research: Oceans, 120, doi:10.1002/2014JC010632. [Full article]
Bai, Y., X. He, D. Pan, C.-T. A. Chen, Y. Kang, X. Chen, and W.-J. Cai (2014). Summertime Changjiang River plume variation during 1998–2010, J. Geophys. Res. Oceans, 119, doi:10.1002/ 2014JC009866. [Full article]
Bailey, S.W., Franz, B.A. and Werdell, P.J. (2010). Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. Optics Express, 18, 7521-7527
Bailey, S.W., Hooker,S.B. Antoine, D., Franz, B.A. and P. Werdell, J. (2008). Sources and assumptions for the vicarious calibration of ocean color satellite observations. Appl. Opt., 47:(12) 2035-2045.
Baird, M.E., Cherukuru, N., Jones, E., Margvelashvili, N., Mongin, M., Oubelkheir, K., Ralph, P.J., Rizwi, F., Robson, B.J., Schroeder, T. and Skerratt, J. (2016). Remote-sensing reflectance and true colour produced by a coupled hydrodynamic, optical, sediment, biogeochemical model of the Great Barrier Reef, Australia: Comparison with satellite data. Environmental Modelling & Software, 78: 79-96. http://dx.doi.org/10.1016/j.envsoft.2015.11.025
Bak, S., Kim, H.-M., Kim, Y., Lee, I., Park, M., Kim, T.-Y. and Jang Seon, W. (2024) High-resolution mapping techniques for coastal debris using YOLOv8 and unmanned aerial vehicle. KJRS, vol 40 (2), pp 151-166, https://doi.org/10.7780/kjrs.2024.40.2.3
Baker, K.S. and Smith, R.C. (1982). Bio-optical classification and model of natural waters. Limnol. Oceanogr., 27: 500-509.
Balch, W., Evans, R., Brown, J., Feldman, G., McClain, C. and Esaias, W. (1992). The remote sensing of ocean primary productivity: Use of a new data compilation to test satellite algorithms. J. Geophys. Res., 97: 2279-2293.
Balch, W.M., Abbott, M.R. and Eppley, R.W. (1989a). Remote sensing of primary production–I. A comparison of empirical and semi-analytical algorithms. Deep-Sea Res. I., 36: 281-295.
Balch, W.M., Eppley, R.W. and Abbott, M.R. (1989b). Remote sensing of primary production, II. A semi-analytical algorithm based on pigments, temperature and light. Deep-Sea Res. I., 36: 1201-1217.
Balch, W.M., Eppley, R.W., Abbott, M.R. and Reid, F.M.H. (1989c). Bias in satellite-derived pigment measurements due to coccolithophores and dinoflagellates. J. Plank. Res., 11: 575-581.
Balkanski, Y., Monfray, P., Battle, M., and Heimann, M. (1999). Ocean primary production derived from satellite data: an evaluation with atmospheric oxygen measurements. Global Biogeochem. Cycles, 13: 257-271.
Balsi, M., Moroni, M., Chiarabini, V., and Tanda, G.(2021). High-resolution aerial detection of marine plastic litter by hyperspectral sensing, Remote Sens. (Basel), 13, 1557, https://doi.org/10.3390/rs13081557
Bancud, G. E., Labanon, A. J., Abreo, N. A. and Kobayashi, V. (2023). Combining image enhancement techniques and deep learning for shallow water benthic marine litter detection. International Workshops of ECML PKDD 2022, 137-149, https://doi.org/10.1007/978-3-031-23618-1_9
Bansal, K. and Tripathi, A. K. (2024) WasteNet: A novel multi-scale attention-based U-Net architecture for waste detection in UAV images. Remote Sens. Appl. Soc. Environ., vol. 35, p. 101220, https://doi.org/10.1016/j.rsase.2024.101220
Banse, K. and English, D.C. (1993). Revision of satellite-based phytoplankton pigment data from the Arabian Sea during the northeast monsoon. Mar. Res., 2: 83-103.
Banse, K. and English, D.C. (1994). Seasonality of coastal zone color scanner phytoplankton pigment in the offshore oceans. J. Geophys. Res., 99: 7323-7345.
The sixth International Ocean Colour Science (IOCS) meeting will take place in Darmstadt, Germany from 1 – 4 December 2025, hosted by EUMETSAT and ESA with support from other agencies.