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
Hu, C., S. Zhang, B. B. Barnes, Y. Xie, M. Wang, J. P. Cannizzaro, and D. C. English (2023). Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi-band medium-resolution satellite data and deep learning. Remote Sens. Environ., 113515, https://doi.org/10.
Hueni, A., and Bertschi, S. (2020). Detection of sub-pixel plastic abundance on water surfaces using airborne imaging spectroscopy, in IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, 26 Sept.-2 Oct, Waikoloa, HI, USA, 6325-6328, https://doi.org/10.1109/IGARSS39084.2020.9323556
Iordache, M.-D., De Keukelaere, L., Moelans, R., Landuyt, L., Moshtaghi, M., Corradi, P., and Knaeps, E. (2022). Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images, Remote Sens., 14(22), 5820, https://doi.org/10.3390/rs14225820
Jakovljevic, G., Govedarica, M., and Alvarez-Taboada, F.(2020). A deep learning model for automatic plastic mapping using unmanned aerial vehicle (UAV) data, Remote Sens. (Basel), 12, 1515 ( 1511-1521),
http://dx.doi.org/10.3390/rs12091515
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
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
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
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.
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
Kako, S. i., Isobe, A., Kataoka, T., Yufu, K., Sugizono, S., Plybon, C., and Murphy, T. A. (2018). Sequential webcam monitoring and modeling of marine debris abundance, Mar. Pollut. Bull., 132, 33-43, https://doi.org/10.1016/j.marpolbul.2018.04.075
Kako, S. i., Morita, S., and Taneda, T.(2020). Estimation of plastic marine debris volumes on beaches using unmanned aerial vehicles and image processing based on deep learning, Mar. Pollut. Bull., 155, 111127, https://doi.org/10.1016/j.marpolbul.2020.111127.
Kaladharan, P., Vijayakumaran, K., Singh, V., Prema, D., Asha, P. S., Sulochanan, B., Hemasankari, P., Edward, L., Padua, S., Shettigar, V., Anasukoya, A., and Bhint, H. (2017). Prevalence of marine litter along the Indian beaches: A preliminary account on its status and composition, J. Mar. biol. Ass. India, 59, 19-24, https://doi.org/10.6024/jmbai.2017.59.1.1953-03
Kalogirou, E., Makri, D., Kountouri, J., Stylianou, T., Themistokleous, K., Papoutsa, C., Melillos, G., and G. Hadjimitsis, D. (2023) Detect plastic litter in Cyprus region using Sentinel-2, in Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 3-5 April, Ayia Napa, Cyprus, 7, https://doi.org/10.1117/12.2681679
Kanhai, L. D. K., Asmath, H., and Gobin, J. F. (2022) The status of marine debris/litter and plastic pollution in the Caribbean Large Marine Ecosystem (CLME): 1980–2020. Environmental Pollution, v. 300, p. 118919, https://doi.org/10.1016/j.
Karakuş O (2023), On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring. Front. Remote Sens., 4, 1302384, https://doi.org/10.3389/frsen.2023.1302384
Kataoka, T., Hinata, H., and Kako, S. i. (2012). A new technique for detecting colored macro plastic debris on beaches using webcam images and CIELUV, Mar. Pollut. Bull., 64, 1829-1836, https://doi.org/10.1016/j.marpolbul.2012.06.006.
Kataoka, T., Murray, C. C., and Isobe, A. (2018). Quantification of marine macro-debris abundance around Vancouver Island, Canada, based on archived aerial photographs processed by projective transformation, Mar. Pollut. Bull., 132, 44-51, https://doi.org/10.1016/j.marpolbul.2017.08.060
Kikaki, A., Karantzalos, K., Power, C. A., and Raitsos, D.A. (2020). Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea). Remote Sens., 12, 1727, https://doi.org/10.3390/rs12111727
Kikaki, K., Kakogeorgiou, I., Hoteit I., Karantzalos, K. (2024). Detecting Marine pollutants and Sea Surface features with Deep learning in Sentinel-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 210, 39-54. https://doi.org/10.1016/j.isprsjprs.2024.02.017
Kikaki, K., Kakogeorgiou, I., Mikeli, P., Raitsos, DE., Karantzalos, K. (2022). MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data. PLoS ONE 17(1): e0262247. https://doi.org/10.1371/journal.pone.0262247
Kim, J., and Kim, C. W. (2023) Improving the efficiency of marine debris collection policies using drone technology, J. Coastal Res., 116, 353-357, https://doi.org/10.2112/JCR-SI116-072.1
Knaeps, E., Sterckx, S., Strackx, G., Mijnendonckx, J., Moshtaghi, M., Garaba, S. P., and Meire, D. (2021). Hyperspectral reflectance dataset of dry, wet and submerged marine litter, Earth Syst. Sci. Data, 13, 713–730, https://doi.org/10.5194/essd-13-713-2021
Koestner, D., Foster, R., El-Habashi, A., and Cheatham, S. (2024) Measurements of the inherent optical properties of aqueous suspensions of microplastics, Limnol. Oceanogr. Lett., 1-11, https://doi.org/10.1002/lol2.10387
Koestner, D.. Foster, R. and El-Habashi, A. (2023). On the potential for optical detection of microplastics in the ocean. Oceanography, 36, https://doi.org/10.5670/oceanog.2023.s1.15
Kremezi, M., Kristollari, V., Karathanassi, V., Topouzelis, K., Kolokoussis, P., Taggio, N., Aiello, A., Ceriola, G., Barbone, E., Corradi, P. (2021). Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes. IEEE Access,9, 61955-61971, https://doi.org/10.1109/ACCESS.2021.3073903.
Kremezi, M., Kristollari, V., Karathanassi, V., Topouzelis, K., Kolokoussis, P., Taggio, N., Aiello, A., Ceriola, G., Barbone, E., Corradi, P. (2022). Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques. Marine Pollution Bulletin, Volume 182, 2022, 113974, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2022.113974 .
Kruse, C., Boyda, E., Chen, S., Karra, K., Bou-Nahra, T., Hammer, D., Mathis, J., Maddalene, T., Jambeck, J. and Laurier, F. (2023) Satellite monitoring of terrestrial plastic waste. PLoS One, 18, (1), 1-20, e0278997, https://doi.org/10.1371/journal.pone.0278997
Kühn, F., Oppermann, K., and Hörig, B. (2004). Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons, Int. J. Remote Sens., 25(12), 2467-2473, https://doi.org/10.1080/01431160310001642287
Lavender, S. (2022) Detection of waste plastics in the environment: Application of Copernicus earth observation data. Remote Sensing, v. 14, no. 19, p. 4772, https://doi.org/10.3390/rs14194772
Leone, G., Catarino, A. I., De Keukelaere, L., Bossaer, M., Knaeps, E. and Everaert, G. (2023). Hyperspectral reflectance dataset of pristine, weathered, and biofouledplastics. Earth Syst. Sci. Data, 2, 745–52, https://doi.org/10.5194/essd-15-745-2023
Li, J., Liu, H., Liao, R., Wang, H., Chen, Y., Xiang, J., Xu, X. and Ma, H. (2023). Recognition of microplastics suspended in seawater via refractive index by Mueller matrix polarimetry. Mar. Pollut. Bull., 188, 114706, https://doi.org/10.1016/j.marpolbul.2023.114706
Liu, H., Wang, M., Tang, H., and Zhang, H. (2024) Progress in research on marine litter-related monitoring technologies, J. Phys.: Conf. Ser., 2679, 012055, https://doi.org/10.1088/1742-6596/2679/1/012055
Ma, J., Ma, R., Pan, Q., Liang, X., Wang, J., and Ni, X. (2023) A global review of progress in remote sensing and monitoring of marine pollution, Water, 15, 3491, https://doi.org/10.3390/w15193491
Ma, Y., Qu, X., Yu, C., Wu, L., Zhang, P., Huang, H., Gui, F. and Feng, D.(2022). Automatic extraction of marine aquaculture zones from optical satellite images by R3Det with piecewise linear stretching. Remote Sens., 14, (18), 4430, https://doi.org/10.3390/rs14184430
Maharjan, N., Miyazaki, H., Pati, B. M.; Dailey, M. N.; Shrestha, S. and Nakamura, T. (2022). Detection of river plastic using UAV sensor data and deep learning. Remote Sens., 14, (13), 3049, https://doi.org/10.3390/rs14133049
Mahmoud, A., and El-Sharkawy, Y. H. (2024) Instant plastic waste detection on shores using laser-induced fluorescence and associated hyperspectral imaging, Opt. Quant. Electron., 56, 780, https://doi.org/10.1007/s11082-024-06564-8
Mandhati, S. R., Deshapriya, N. L., Mendis, C. L., Gunasekara, K., Yrle, F., Chaksan, A., and Sanjeev, S.(2024) pLitterStreet: Street level plastic litter detection and mapping, arXiv, 1-14, https://doi.org/10.48550/arXiv.2401.14719
Martin, C., Parkes, S., Zhang, Q., Zhang, X., McCabe, M. F., and Duarte, C. M. (2018). Use of unmanned aerial vehicles for efficient beach litter monitoring, Mar. Pollut. Bull., 131, 662-673, https://doi.org/10.1016/j.marpolbul.2018.04.045
Martinez-Vicente, V., Biermann, L., and Mata, A. (2020). Optical methods for marine litter detection (OPTIMAL) – Final Report. Zenodo, https://doi.org/10.5281/zenodo.3748797
Martínez-Vicente, V., Clark, J. R., Corradi, P., Aliani, S., Arias, M., Bochow, M., Bonnery, G., Cole, M., Cózar, A., Donnelly, R., Echevarría, F., Galgani, F., Garaba, S. P., Goddijn-Murphy, L., Lebreton, L., Leslie, H. A., Lindeque, P. K., Maximenko, N., Martin-Lauzer, F.-R., Moller, D., Murphy, P., Palombi, L., Raimondi, V., Reisser, J., Romero, L., Simis, S. G. H., Sterckx, S., Thompson, R. C., Topouzelis, K. N., van Sebille, E., Veiga, J. M., and Vethaak, A. D. (2019). Measuring marine plastic debris from space: Initial assessment of observation requirements, Remote Sens. (Basel), 11, 2443, doi:10.3390/rs11202443, https://www.mdpi.com/2072-4292/11/20/2443
Matthews, J. P., Ostrovsky, L., Yoshikawa, Y., Komori, S. and Tamura, H. (2017) Dynamics and early post-tsunami evolution of floating marine debris near Fukushima Daiichi. Nature Geosci., 10, 598-603, https://doi.org/10.1038/ngeo2975
Maximenko, N., Arvesen, J., Asner, G., Carlton, J., Castrence, M., Centurioni, L., Chao, Y., Chapman, J., Chirayath, V., Corradi, P., Crowley, M., Dierssen, H. M., Dohan, K., Eriksen, M., Galgani, F., Garaba, S. P., Goni, G., Griffin, D., Hafner, J., Hardesty, D., Isobe, A., Jacobs, G., Kamachi, M., Kataoka, T., Kubota, M., Law, K. L., Lebreton, L., Leslie, H. A., Lumpkin, R., Mace, T. H., Mallos, N., McGillivary, P. A., Moller, D., Morrow, R., Moy, K. V., Murray, C. C., Potemra, J., Richardson, P., Robberson, B., Thompson, R., van Sebille, E., and Woodring, D (2016). Remote sensing of marine debris to study dynamics, balances and trends, Community White Paper Produced at the Workshop on Mission Concepts for Marine Debris Sensing, 22 pp, 2016.
Maximenko, N., Corradi, P., Law, K. L., Van Sebille, E., Garaba, S. P., Lampitt, R. S., Galgani, F., Martinez-Vicente, V., Goddijn-Murphy, L., Veiga, J. M., Thompson, R. C., Maes, C., Moller, D., Löscher, C. R., Addamo, A. M., Lamson, M. R., Centurioni, L. R., Posth, N. R., Lumpkin, R., Vinci, M., Martins, A. M., Pieper, C. D., Isobe, A., Hanke, G., Edwards, M., Chubarenko, I. P., Rodriguez, E., Aliani, S., Arias, M., Asner, G. P., Brosich, A., Carlton, J. T., Chao, Y., Cook, A.-M., Cundy, A. B., Galloway, T. S., Giorgetti, A., Goni, G. J., Guichoux, Y., Haram, L. E., Hardesty, B. D., Holdsworth, N., Lebreton, L., Leslie, H. A., Macadam-Somer, I., Mace, T., Manuel, M., Marsh, R., Martinez, E., Mayor, D. J., Le Moigne, M., Molina Jack, M. E., Mowlem, M. C., Obbard, R. W., Pabortsava, K., Robberson, B., Rotaru, A.-E., Ruiz, G. M., Spedicato, M. T., Thiel, M., Turra, A., and Wilcox, C. (2019). Toward the Integrated Marine Debris Observing System, Front. Mar. Sci., 6, doi:10.3389/fmars.2019.00447. https://www.frontiersin.org/articles/10.3389/fmars.2019.00447/full
Merlino, S., Paterni, M., Berton, A., and Massetti, L. (2020). Unmanned aerial vehicles for debris survey in coastal areas: Long-term monitoring programme to study spatial and temporal accumulation of the dynamics of beached marine litter. Remote Sens.(Basel), 12(8), 1260, https://doi.org/10.3390/rs12081260
Merlino, S., Paterni, M., Locritani, M., Andriolo, U., Gonçalves, G., Massetti, L. (2021). Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images. Water, 13(23):3349. https://doi.org/10.3390/w13233349
Mifdal, J., Longépé, N. and Rußwurm, M. (2021) Towards detecting floating objects on a global scale with learned spatial features using Sentinel 2. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 285-293, https://doi.org/10.5194/isprs-annals-V-3-2021-285-2021
Mohsen, A., Kiss, T. and Kovács, F. (2023) Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery. Environ. Sci. Pollut. Res., 30, 67742–67757, https://doi.org/10.1007/s11356-023-27068-0