The IOCCG bibliography is updated periodically when new references are submitted by readers, especially references that are published open access. 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 open access publication to be included in the Ocean Colour 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 as a link. Please also check that 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
Jamet, C., Thiria, S., Moulin, C. and Crepon, M. (2005). Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from ocean color imagery: a feasibility study. J. Atmos. Ocean. Tech., 22(4): 460-475, doi:10.1175/JTECH1688.1.
Jamet, C.; Ibrahim, A.; Ahmad, Z.; Angelini, F.; Babin, M.; Behrenfeld, M. J.; Boss, E.; Cairns, B.; Churnside, J.; Chowdhary, J.; Davis, A. B.; Dionisi, D.; Duforêt-Gaurier, L.; Franz, B.; Frouin, R.; Gao, M.; Gray, D.; Hasekamp, O.; He, X.; Hostetler, C.; Kalashnikova, O. V.; Knobelspiesse, K.; Lacour, L.; Loisel, H.; Martins, V.; Rehm, E.; Remer, L.; Sanhaj, I.; Stamnes, K.; Stamnes, S.; Victori, S.; Werdell, J. & Zhai, P.-W. (2019) Going Beyond Standard Ocean Color Observations: Lidar and Polarimetry. Frontiers in Marine Science, 6, 251. https://doi.org/10.3389/fmars.2019.00251
Janssens, N., Schreyers, L., Biermann, L., van der Ploeg, M., Le Bui, T.-K. and van Emmerik, T. (2022). Rivers running green: water hyacinth invasion monitored from space. Environmental Research Letters. 17 (4), https://doi.org/10.1088/1748-9326/ac52c
Jaquet, J.-M., Schanz, F., Bossard, P., Hanselmann, K. and Gendre, F. (1994). Measurement and significance of bio-optical parameters for remote sensing in two subalpine lakes of different tropic state. Aquat. Sci. 56: 263-305.
Jayabhavani, G. N. and Tamilarasi, M. (2024) Floating Litter detection at the Estuary of Puducherry using Sentinel-2 data and Machine Learning model. Reg. Stud. Mar. Sci., 103686, https://doi.org/10.1016/j.rsma.2024.103686
Jayasiri, H.B., Purushothaman, C.S., and Vennila, A (2013). Quantitative analysis of plastic debris on recreational beaches in Mumbai, India, Mar. Pollut. Bull., 77, 107-112, https://doi.org/10.1016/j.marpolbul.2013.10.024
Jeffrey, S. W., Wright, S. W., and Zapata, M. (1999). Recent advances in HPLC pigment analysis of phytoplankton. Mar. Freshwater Res. 50: 879-896.
Jeong, Y., Shin, J., Lee, J.-S., Baek, J.-Y., Schläpfer, D., Kim, S.-Y., Jeong, J.-Y. and Jo, Y.-H. (2024) A study on the monitoring of floating marine macro-litter using a multi-spectral sensor and classification based on deep learning. Remote Sens., v. 16, no. 23, p. 4347. https://doi.org/10.3390/rs16234347
Jia, T., Kapelan, Z., de Vries, R., Vriend, P., Peereboom, E. C., Okkerman, I. and Taormina, R., (2023). Deep learning for detecting macroplastic litter in water bodies: A review. Water Res., 231, 119632, https://doi.org/10.1016/j.watres.2023.119632
Jia, T., Vallendar, A. J., de Vries, R., Kapelan, Z., and Taormina, R. (2023) Advancing deep learning-based detection of floating litter using a novel open dataset, Front. Water, 5, 1298465, https://doi.org/10.3389/frwa.2023.1298465
Jiang B, Boss E, Kiffney T, Hesketh G, Bourdin G, Fan D and Brady DC (2022) Oyster Aquaculture Site Selection Using High-Resolution Remote Sensing: A Case Study in the Gulf of Maine, United States. Front. Mar. Sci. 9:802438. http://doi.org/10.3389/fmars.2022.802438
Jiang, L. and M. Wang (2014). Improved near-infrared ocean reflectance correction algorithm for satellite ocean color data processing. Opt. Express, 22, 21657-21678. doi:10.1364/OE.22.021657
Jiang, L. D., and M. H. Wang (2013). Identification of pixels with stray light and cloud shadow contaminations in the satellite ocean color data processing, Appl. Optics, 52(27), 6757-6770, Doi 10.1364/Ao.52.006757.
Jiao, J., Y. Lu, and C. Hu (2023). Optical interpretation of oil emulsions in the ocean – Part III: A three-dimensional unmixing model to quantify oil concentration. Remote Sens. Environ., 296, 113719, https://doi.org/10.
Jiao, J., Y. Lu, and C. Hu (2024). Characterizing oil spills using deep learning and spectral-spatial-geometrical features of HY-1C/D CZI images. Remote Sens. Environ., 308, 114205, https://doi.org/10.
Jin, Z., T.P. Charlock, W.L. Smith, Jr., C.K. Rutledge, G.F. Cota, R. Kahn, J. Redemann, T. Zhang, D. Rutan, and F. Rose (2004). Radiation measurement and model simulation for CLAMS. J. Atmos. Sci.
Jitts, H., A. Morel And Y. Saijo, 1977. The relation of oceanic primary production to available photosynthetic irradiance. Aust. J. Mar. Freshwater Res., 27, 441-454.
Johannessen, S.C., Miller, W.L., and Cullen, J.J.(2003). Calculation of UV attenuation and colored dissolved organic matter absorption spectra from measurements of ocean color. J. Geophys. Res., 108(C9): 3301 10.1029/2000JC000514
Johnsen, G. and Sakshaug, E. (1993). Bio-optical characteristics and photoadaptive responses in the toxic and bloom–forming dinoflagellates Gyrodinium aureolum, Gymnodinium galatheanum, and two strains of Prorocentrum minimum. J. Phycol. 29: 627-642.
Johnsen, G., Nelson, N.B., Jovine, R.V.M. and Prézelin, B.B. (1994). Chromoprotein- and pigment–dependent modeling of spectral light absorption in two dinoflagellates Prorocentrum minimum and Heterocapsa pygmaea. Mar. Ecol. Prog. Ser. 114: 245-258.
Johnsen, G., Sakshaug, E. (2000). Monitoring of harmful algal blooms along the Norwegian coast using bio-optical methods. S. Afr. J. Mar. Sci. (22) 309-321.
Johnson, D.B., Flament, P. and Bernstein, R.L. (1994). High-resolution satellite imagery for mesoscale meteorological studies. Bull. Am. Meteorol. Soc. 75: 5-33.
Johnson, D.R., Miller, J., and Schofield, O. (2003). Dynamics and optics of the Hudson River outflow plume J. Geophys. Res.,108(C10): 3323 10.1029/2002JC001485
Johnson, D.R., Weidemann, A., Arnone, R., and Davis, C.O. (2001) Chesapeake Bay outflow plume and coastal upwelling events: Physical and optical properties J. Geophys. Res. Vol. 106 (No.C6), p. 11,613.
Joint, I., and Groom, S. B. (2000). Estimation of phytoplankton production from space: current status and future potential of satellite remote sensing. J. Exp. Mar. Biol. Ecol. 250: 233-255.
Jordan, T. M., Dall’Olmo, G., Tilstone, G., Brewin, R. J. W., Nencioli, F., Airs, R., Thomas, C. S., and Schlüter, L. (2025) A compilation of surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect, Earth Syst. Sci. Data, 17, 493–516. https://doi.org/10.5194/essd-17-493-2025
Jordan, M.B. (1988). A new submersible recording scalar light sensor array. Deep-Sea Res.I. 35: 1411-1423.
Jorde, L. B. and Harpending, H. C. (1976). Cross-spectral analysis of rainfall and human birth rate: an empirical test of a linear model. J. Hum. Evol. 5: 129-138.
Jorge, Daniel S.F., Hubert Loisel, Cédric Jamet, David Dessailly, Julien Demaria, Annick Bricaud, Stéphane Maritorena, Xiaodong Zhang, David Antoine, Tiit Kutser, Simon Bélanger, Vittorio O. Brando, Jeremy Werdell, Ewa Kwiatkowska, Antoine Mangin, Odile Fanton d’Andon (2021).
A three-step semi analytical algorithm (3SAA) for estimating inherent optical properties over oceanic, coastal, and inland waters from remote sensing reflectance,
Remote Sens. Environ., 263, 112537. https://doi.org/10.1016/j.rse.2021.112537
Jorgensen, P.V. (1999). Standard CZCS Case 1 algorithms in Danish coastal waters. Intl. J. Remote Sens. 20(7): 1289-1301.
Ortiz, J.D., Dulci M. Avouris, Stephen J. Schiller, Jeffrey C. Luvall, John D. Lekki, Roger P. Tokars, Robert C. Anderson, Robert Shuchman, Michael Sayers, Richard Becker (2019). Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie. Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.03.005.
Jourdin, F., Renosh, P.R., Charantonis, A.A. , Guillou, N., Thiria, S., Badran, F. and Garlan, T. (2020). An Observing System Simulation Experiment (OSSE) in Deriving Suspended Sediment Concentrations in the Ocean From MTG/FCI Satellite Sensor,” in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3011742. https://ieeexplore.ieee.org/document/9165152/keywords#keywords
Juhls, B., A. Matsuoka, M. Lizotte, G. Bécu, P.P. Overduin, J. El Kassar, E. Devred, D. Doxaran, J. Ferland, M.H. Forget, A. Hilborn, M. Hieronymi, E. Leymarie, J. Maury, L. Oziel, L. Tisserand, D.O.J. Anikina, M. Dillon, M. Babin (2022). Seasonal dynamics of dissolved organic matter in the Mackenzie Delta, Canadian Arctic waters: Implications for ocean colour remote sensing. Remote Sensing of Environment, 283, 113327. https://doi.org/10.1016/j.rse.2022.113327
Jupp, D. L. B., Kirk, J. T. O. and Harris, G. P. (1994). Detection, identification and mapping of cyanobacteria — Using remote sensing to measure the optical quality of turbid inland waters. Aust. J. Mar. Freshw. Res. 45: 801-828.
Kahru, M. and Elmgren, R. (2014). Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea. Biogeosciences, 11: 3619-3633. [Abstract]
Kahru, M. and Mitchell, B. G. (1998). Spectral reflectance and absorption of a massive red tide off Southern California. J. Geophys. Res., 103 (C10): 21,601-21,609.
Kahru, M. and Mitchell, B. G. (1999). Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current. Intl. J. Remote Sens., 20(N17): 3423-3429.
Kahru, M. and Mitchell, B.G. (2008). Ocean color reveals increased blooms in various parts of the World. EOS, Trans. AGU, 89(18): 170. [ PDF file]
Kahru, M. Marinone, S.G., Lluch-Cota, S. E., Parés-Sierra, A and Mitchell, B.G. (2004). Ocean-color variability in the Gulf of California: scales from days to ENSO. Deep Sea Research. II. Topical Studies in Oceanography
Kahru, M., B.G. Mitchell. (2000). Influence of the 1997-98 El Niño on the surface chlorophyll in the California Current, Geophysical Research Letters, Vol. 27, No. 18, 2937-2940.
Kahru, M., B.G. Mitchell. (2001). Seasonal and non-seasonal variability of satellite-derived chlorophyll and CDOM concentration in the California Current. J. Geophys. Res., 106(C2): 2517-2529.
Kahru, M., Fiedler, P. C. Gille, S. T. Manzano, M. and Mitchell, B. G. (2007). Sea level anomalies control phytoplankton biomass in the Costa Rica Dome area. Geophys. Res. Lett., 34: L22601, doi:10.1029/2007GL031631. [PDF file]
Kahru, M., J.-M. Leppänen, O.Rud, O.P. Savchuk. (2000). Cyanobacteria blooms in the Gulf of Finland triggered by saltwater inflow into the Baltic Sea. Marine Ecolgy Progress Series, 207:13-18.
Kahru, M., Mitchell, B.G. (2002). Influence of the El Niño – La Niña cycle on satellite-derived primary production in the California Current. Geophys. Res. Let., 29 (9)
Kahru, M., Mitchell,B. G. Gille,S. T. Hewes, C. D. and Holm-Hansen, O. (2007). Eddies enhance biological production in the Weddell-Scotia Confluence of the Southern Ocean. Geophys. Res. Let., 34: L14603, doi:10.1029/2007GL030430. [PDF file]
Kahru, M., Savchuk, O. P. and Elmgren, R. (2007). Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability. Mar. Ecol. Prog. Ser., 343:15-23, doi: 10.3354/meps06943. [PDF file]
Kajiyama, T., D’Alimonte, D. ; Zibordi, G. (2013). Regional Algorithms for European Seas: A Case Study Based on MERIS Data. Geoscience and Remote Sensing Letters, IEEE, 10(2): 283 – 287. doi: 10.1109/LGRS.2012.2202370. [Abstract]
Kajiyama, T., D’Alimonte, D. ; Zibordi, G. (2014). Match-Up Analysis of MERIS Radiometric Data in the Northern Adriatic Sea. Geoscience and Remote Sensing Letters, IEEE, 11(1): 19-23. doi:10.1109/LGRS.2013.2244844. [Abstract]
Kako, S. i., Isobe, A., and Magome, S. (2012). Low altitude remote-sensing method to monitor marine and beach litter of various colors using a balloon equipped with a digital camera, Mar. Pollut. Bull., 64(6), 1156-1162, https://doi.org/10.1016/j.marpolbul.2012.03.024
Kako, S. i., Isobe, A., Kataoka, T., and Hinata, H. (2014). A decadal prediction of the quantity of plastic marine debris littered on beaches of the East Asian marginal seas, Mar. Pollut. Bull., 81, 174-184, https://doi.org/10.1016/j.marpolbul.2014.01.057
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.