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
Cortesi, I., Mugnai, F., Angelini, R., and Masiero, A. (2023) Mini UAV-based litter detection on river banks, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4/W1-2022, 117-122, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-117-2023
Costa, A., Sans, E., Pereira-Sánchez, I., Duran, J. and Navarro, J. (2024) In Improving marine litter segmentation with limited resolution satellite imagery, 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), 8-10 April 2024, 1-3.; https://doi.org/10.1109/MIGARS61408.2024.10544681
Cózar, A., Aliani, S., Basurko, O.C., Arias, M., Isobe, A., Topouzelis, K., Rubio, A., and Morales-Caselles, C. (2021). Marine litter windrows: A strategic target to understand and manage the ocean plastic pollution, Front. Mar. Sci., 8, 571796, https://doi.org/10.3389/fmars.2021.571796
da Costa, T. S., Felício, J. M., Vala, M., Leonor, N., Costa, J. R., Marques, P., Moreira, A. A., Caldeirinha, R., Matos, S. A., Fernandes, C. A., Fonseca, N. J. G., and Maagt, P. d. (2023) Detection of low permittivity floating plastic sheets at microwave frequencies, in 2023 17th European Conference on Antennas and Propagation (EuCAP), 1-5, https://doi.org/10.23919/EuCAP57121.2023.10133107
da Costa, T. S., Felício, J. M., Vala, M., Leonor, N., Costa, J. R., Marques, P., Moreira, A. A., Caldeirinha, R., Matos, S. A., Fernandes, C. A., Fonseca, N. J. G., Maagt, P. D. (2024) In Feature selection for identifying optimal microwave frequencies to detect floating macroplastic litter in C and X Bands, 2024 18th European Conference on Antennas and Propagation (EuCAP), Glasgow, United Kingdom, 17-22 March 2024, Glasgow, United Kingdom, 2024, pp 1-5, https://doi.org/10.23919/EuCAP60739.2024.10501024
Danilov, A., and Serdiukova, E. (2024) Review of methods for automatic plastic detection in water areas using satellite images and machine learning, Sensors, vol. 24(16), pp 5089, https://doi.org/10.3390/s24165089
Dasgupta, S., Sarraf, M., and Wheeler, D. (2022) Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data. Sci. Total Environ., v. 839, p. 156319, DOI: https://doi.org/10.1016/j.scitotenv.2022.156319
de Fockert, A., Eleveld, M.A., Bakker, W., Felício, J.M., Costa, T.S., Vala, M., Marques, P., Leonor, N., Moreira, A., Costa, J.R., Caldeirinha, R.F.S., Matos, S.A., Fernandes, C.A., Fonseca, N., Simpson, M.D., Marino, A., Gandini, E., Camps, A., Perez Portero, A., Gonga, A., Burggraaff, O., Garaba, S.P., Salama, M.S., Xiao, Q., Calvert, R., van den Bremer, T.S., de Maagt, P., 2024. Assessing the detection of floating plastic litter with advanced remote sensing technologies in a hydrodynamic test facility. Sci Rep, 14, 25902, https://doi.org/10.1038/s41598-024-74332-5
De Giglio, M., Dubbini, M., Cortesi, I., Maraviglia, M., Parisi, E. I. and Tucci, G. (2021) Plastics waste identification in river ecosystems by multispectral proximal sensing: a preliminary methodology study. Water Environ. J., vol 35(2), pp 569-579, https://doi.org/10.1111/wej.12652
de Vries, R. V. F., Garaba, S. P., and Royer, S. J.(2023) Hyperspectral reflectance of pristine, ocean weathered and biofouled plastics from a dry to wet and submerged state, Earth Syst. Sci. Data, 15, 5575-5596, https://doi.org/10.5194/essd-15-5575-2023
Deidun, A., Gauci, A., Lagorio, S., and Galgani, F. (2018). Optimising beached litter monitoring protocols through aerial imagery, Mar. Pollut. Bull., 131, 212-217, https://doi.org/10.1016/j.marpolbul.2018.04.033
de Vries, R., Egger, M., Mani, T. and Lebreton, L. (2021). Quantifying floating plastic debris at sea using vessel-based optical data and artificial intelligence. Remote Sensing, 13(17), 3401, https://doi.org/10.3390/rs13173401
Dhanishtaa, R., Elakkiya, E., Gunashri, R.,and Madhumathi, R. (2024) Plastic litter detection using YOLOv8 algorithm, in 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 429-433, https://doi.org/10.1109/ICSSAS64001.2024.10760932
Dierssen, H. M., and Garaba, S. P.: (2020). Bright Oceans: Spectral Differentiation of Whitecaps, Sea Ice, Plastics, and Other Flotsam, in: Recent Advances in the Study of Oceanic Whitecaps: Twixt Wind and Waves, edited by: Vlahos, P., and Monahan, E. C., Springer International Publishing, Cham, 197-208, 2020. https://doi.org/10.1007/978-3-030-36371-0_13
Duarte, D., Andriolo, U., Gonçalves, G. (2020). Addressing the class imbalance problem in the automatic image classification of coastal litter from orthophotos derived from uas imagery, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 439–445, https://doi.org/10.5194/isprs-annals-V-3-2020-439-2020
Duarte, M. M., and Azevedo, L. (2023) Automatic detection and identification of floating marine debris using multi-spectral satellite imagery. IEEE Trans. Geosci. Remote Sens., 1-15, https://doi.org/10.1109/TGRS.2023.3283607
Duplančić Leder, T., Leder, N., Baučić, M. and Bačić, S. (2024) Optical remote sensing methods for floating marine debris detection – Review and bibliography analysis. Transactions on Maritime Science, v. 13, no. 2, p. 005, https://doi.org/10.7225/toms.v13.n02.005
Evans, M. C. and C. S. Ruf (2021). Towards the detection and imaging of ocean microplastics with a spaceborne radar. IEEE Transactions on Geoscience and Remote Sensing, 1-9,https://doi.org/10.1109/TGRS.2021.3081691
Fallati, L., Polidori, A., Salvatore, C., Saponari, L., Savini, A., and Galli, P.(2019). Anthropogenic marine debris assessment with unmanned aerial vehicle imagery and deep learning: A case study along the beaches of the Republic of Maldives, Sci. Total Environ., 693, 133581, https://doi.org/10.1016/j.scitotenv.2019.133581
Farré, M. (2020) Remote and in situ devices for the assessment of marine contaminants of emerging concern and plastic debris detection. Curr. Opin. Environ. Sci. Health, vol 18, pp 79-94, https://doi.org/10.1016/j.coesh.2020.10.002
Felício, J. M., Costa, T. S., Vala, M., Leonor, N., Costa, J. R., Marques, P., Moreira, A. A., Caldeirinha, R. F. S., Matos, S. A., Fernandes, C. A., Fonseca, N. J. G., and Maagt, P. D. (2024) Feasibility of radar-based detection of floating macroplastics at microwave frequencies, IEEE Trans. Antennas Prop., 1-1, https://doi.org/10.1109/TAP.2023.3347031
Ferral A, Bonansea M, Scavuzzo CM, Nemiña F, Burgos Paci M, Ramirez JC, Sepúlveda B, Fraxedas J and Esplandiu MJ (2024), Bringing satellite and nanotechnologies together: unifying strengths against pollution and climate change. Front. Nanotechnol. 6:1332820. https://doi.org/10.3389/fnano.2024.1332820
Freitas, S., Silva, H., and Silva, E. (2021). Remote hyperspectral imaging acquisition and characterization for marine litter detection, Remote Sens., 13, 2536(2531-2522), https://doi.org/10.3390/rs13132536.
Freitas, S., Silva, H., and Silva, E. (2022). Hyperspectral imaging zero-shot learning for remote marine litter detection and classification, Remote Sens. (Basel), 14, 5516(5511-5518), https://doi.org/10.3390/rs14215516.
Fronkova, L., Brayne, R. P., Ribeiro, J. W., Cliffen, M., Beccari, F. and Arnott, J. H. W. (2024) Assessing the effect of water on submerged and floating plastic detection using remote sensing and K-Means clustering. Remote Sens., v. 16, no. 23, p. 4405. https://doi.org/10.3390/rs16234405
Garaba S.P.and Dierssen H.M. (2018). An airborne remote sensing case study of synthetic hydrocarbon detection using short wave infrared absorption features identified from marine-harvested macro- and microplastics. Remote Sensing of Environment 205:224-235. doi:10.1016/j.rse.2017.11.023. www.sciencedirect.com/science/article/pii/S0034425717305722
Garaba, S. P. and Dierssen, H. M. (2020). Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics, Earth Syst. Sci. Data, 12, 77–86, https://doi.org/10.5194/essd-12-77-2020
Garaba, S. P., Acuña-Ruz, T., and Mattar, C. B. (2020). Hyperspectral longwave infrared reflectance spectra of naturally dried algae, anthropogenic plastics, sands and shells, Earth Syst. Sci. Data, 12, 2665-2678, https://doi.org/10.5194/essd-12-2665-2020
Garaba, S. P., and Harmel, T. (2022) Top-of-atmosphere hyper and multispectral signatures of submerged plastic litter with changing water clarity and depth. Opt. Express, 30 (10), 16553-16571, https://doi.org/10.1364/OE.
Garaba, S. P., and Park, Y.-J. (2024) Riverine litter monitoring from multispectral fine pixel satellite images, Environ. Adv., 15, 100451, https://doi.org/10.1016/j.envadv.2023.100451
Garaba, S. P., Arias, M., Corradi, P., Harmel, T., de Vries, R., and Lebreton, L. (2021) Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics, J. Hazard. Mater., 406, 124290, https://doi.org/10.1016/j.jhazmat.2020.124290
Garaba, S. P., Aitken, J., Slat, B., Dierssen, H.M., Lebreton, L., Zielinski, O. and Reisser, J. (2018). Sensing Ocean Plastics with an Airborne Hyperspectral Shortwave Infrared Imager. Environmental Science & Technology. DOI: 10.1021/acs.est.8b02855
Garcia-Garin, O., Aguilar, A., Borrell, A., Gozalbes, P., Lobo, A., Penadés-Suay, J., Raga, J. A., Revuelta, O., Serrano, M., and Vighi, M. (2020). Who’s better at spotting? A comparison between aerial photography and observer-based methods to monitor floating marine litter and marine mega-fauna, Environ. Pollut., 258, 113680, https://doi.org/10.1016/j.envpol.2019.113680.
Garcia-Garin, O., Monleón-Getino, T., López-Brosa, P., Borrell, A., Aguilar, A., Borja-Robalino, R., Cardona, L., and Vighi, M. (2021). Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R, Environ. Pollut., 273, 116490,
https://doi.org/10.1016/j.envpol.2021.116490.
Ge, Z., Shi, H., Mei, X., Dai, Z., and Li, D. (2016). Semi-automatic recognition of marine debris on beaches, Sci. Rep., 6, 25759, https://doi.org/10.1038/srep25759.
Geraeds, M.; van Emmerik, T.; de Vries, R. and bin Ab Razak, M.S. (2019). Riverine Plastic Litter Monitoring Using Unmanned Aerial Vehicles (UAVs). Remote Sens. 11, 2045, https://doi.org/10.3390/rs11172045
Gnann, N., Baschek, B., Ternes, T. A. (2022). Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review. Water Research: 222, 118902, https://doi.org/10.1016/j.watres.2022.118902 .
Goddijn-Murphy, L., Martínez-Vicente, V., Dierssen, H.M., Raimondi, V., Gandini, E., Foster, R., Chirayath, V. (2024) Emerging Technologies for Remote Sensing of Floating and Submerged Plastic Litter. Remote Sens. 16(10), 1770; doi.org/10.3390/rs16101770.
Goddijn-Murphy, L., Williamson, B. (2019). On Thermal Infrared Remote Sensing of Plastic Pollution in Natural Waters, Remote Sens., 11, 18: 2159. https://doi.org/10.3390/rs11182159
Goddijn-Murphy, L., Williamson, B.J., McIlvenny, J., Corradi, P. (2022). Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter. Remote Sens. 14, 13:3179. https://doi.org/10.3390/rs14133179
Goddijn-Murphy, L.M., Dufaur, J. (2018). Proof of concept for a model of light reflectance of plastics floating on natural waters, Mar. Pollut. Bull., 135, 1145-1157. https://doi.org/10.1016/j.marpolbul.2018.08.044
Goddijn-Murphy, L.M., Peters, S., Van Sebille, E., James, N. A., Gibb, S. (2018). Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics, Mar. Pollut. Bull., 126, 255–262. https://doi.org/10.1016/j.marpolbul.2017.11.011
Gómez, À. S., Scandolo, L., and Eisemann, E. (2022) A learning approach for river debris detection. Int. J. Appl. Earth Obs. Geoinformation, 107, 102682, https://doi.org/10.1016/j.jag.2022.102682
Gonçalves, G. and Andriolo, U. (2022). Operational use of multispectral images for macro-litter mapping and, categorization by Unmanned Aerial Vehicle. Marine Pollution Bulletin. 176, March 2022, 113431 https://doi.org/10.1016/j.marpolbul.2022.113431
Gonçalves, G., Andriolo, U., Gonçalves, L., Sobral, P., Bessa, F. (2020). Quantifying marine macro litter abundance on a sandy beach using unmanned aerial systems and object-oriented machine learning methods. Remote Sensing, 12: 2599. https://doi.org/10.3390/rs12162599
Gonçalves, G., Andriolo, U., Gonçalves, L., Sobral, P., Bessa, F. (2022). Beach litter survey by drones: Mini-review and discussion of a potential standardization. Environmental Pollution, 315, 120370. https://doi.org/10.1016/j.envpol.2022.120370
Gonçalves, G., Andriolo, U., Pinto, L., and Bessa, F. (2020). Mapping marine litter using UAS on a beach-dune system: a multidisciplinary approach. Sci. Total Environ., 706, 135742, https://doi.org/10.1016/j.scitotenv.2019.135742
Gonçalves, G., Andriolo, U., Pinto, L., Duarte, D. (2020). Mapping marine litter with Unmanned Aerial Systems : A showcase comparison among manual image screening and machine learning techniques. Marine Pollution Bulletin. 155, 111158. https://doi.org/10.1016/j.marpolbul.2020.111158
Gonga, A., Pérez-Portero, A., Camps, A., Pascual, D., de Fockert, A. and de Maagt, P. (2023) GNSS-R observations of marine plastic litter in a water flume: An experimental study. Remote Sens., vol 15 (3), pp 637, https://doi.org/10.3390/rs15030637
Gonzaga, M. L. R., Wong, M. T. S., Blanco, A. C., and Principe, J. A. (2021) Utilization of Sentinel-2 imagery in the estimation of plastics among floating debris along the coast of Manila Bay, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 177-184, https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-177-2021
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.