{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T08:09:37Z","timestamp":1772870977185,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NWO Open Mind","award":["18127"],"award-info":[{"award-number":["18127"]}]},{"name":"Veni research program The River Plastic Monitoring Project","award":["18211"],"award-info":[{"award-number":["18211"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems.<\/jats:p>","DOI":"10.3390\/rs13122335","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T21:24:29Z","timestamp":1623792269000},"page":"2335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2007-3456","authenticated-orcid":false,"given":"Paolo","family":"Tasseron","sequence":"first","affiliation":[{"name":"Hydrology and Quantitative Water Management Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4773-9107","authenticated-orcid":false,"given":"Tim","family":"van Emmerik","sequence":"additional","affiliation":[{"name":"Hydrology and Quantitative Water Management Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands"}]},{"given":"Joseph","family":"Peller","sequence":"additional","affiliation":[{"name":"Plant Sciences Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2607-1494","authenticated-orcid":false,"given":"Louise","family":"Schreyers","sequence":"additional","affiliation":[{"name":"Hydrology and Quantitative Water Management Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands"}]},{"given":"Lauren","family":"Biermann","sequence":"additional","affiliation":[{"name":"Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s12302-018-0139-z","article-title":"Marine litter plastics and microplastics and their toxic chemicals components: The need for urgent preventive measures","volume":"30","author":"Gallo","year":"2018","journal-title":"Environ. Sci. Eur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1126\/science.aba3656","article-title":"Predicted growth in plastic waste exceeds efforts to mitigate plastic pollution","volume":"369","author":"Borrelle","year":"2020","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"eaaz5803","DOI":"10.1126\/sciadv.aaz5803","article-title":"More than 1000 rivers account for 80% of global riverine plastic emissions into the ocean","volume":"7","author":"Meijer","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.envpol.2018.11.025","article-title":"Plastic Pirates sample litter at rivers in Germany\u2014Riverside litter and litter sources estimated by schoolchildren","volume":"245","author":"Kiessling","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"298","DOI":"10.3389\/feart.2020.00298","article-title":"Crowd-based observations of riverine macroplastic pollution","volume":"8","author":"Seibert","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.envpol.2014.09.001","article-title":"Assessment of floating plastic debris in surface water along the Seine River","volume":"195","author":"Gasperi","year":"2014","journal-title":"Environ. Pollut."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.envpol.2018.02.005","article-title":"Macroplastic and microplastic contamination assessment of a tropical river (Saigon River, Vietnam) transversed by a developing megacity","volume":"236","author":"Lahens","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tasseron, P., Zinsmeister, H., Rambonnet, L., Hiemstra, A.-F., Siepman, D., and van Emmerik, T. (2020). Plastic Hotspot Mapping in Urban Water Systems. Geosciences, 10.","DOI":"10.3390\/geosciences10090342"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124051","DOI":"10.1088\/1748-9326\/ab5468","article-title":"Abundance of plastic debris across European and Asian rivers","volume":"14","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3791","DOI":"10.3389\/frwa.2020.563791","article-title":"Same but different: A framework to design and compare riverbank plastic monitoring strategies","volume":"2","author":"Vriend","year":"2020","journal-title":"Front. Water"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4932","DOI":"10.1021\/acs.est.0c08094","article-title":"Disentangling Variability in Riverbank Macrolitter Observations","volume":"55","author":"Roebroek","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e2019EA000960","DOI":"10.1029\/2019EA000960","article-title":"Automated River Plastic Monitoring Using Deep Learning and Cameras","volume":"7","author":"Postma","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"77","DOI":"10.5194\/essd-12-77-2020","article-title":"Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics","volume":"12","author":"Garaba","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_14","first-page":"569","article-title":"Plastics waste identification in river ecosystems by multispectral proximal sensing: A preliminary methodology study","volume":"35","author":"Dubbini","year":"2020","journal-title":"Water Environ. J."},{"key":"ref_15","first-page":"175","article-title":"Detection of floating plastics from satellite and unmanned aerial systems (Plastic Litter Project 2018)","volume":"79","author":"Topouzelis","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Themistocleous, K., Papoutsa, C., Michaelides, S., and Hadjimitsis, D. (2020). Investigating Detection of Floating Plastic Litter from Space Using Sentinel-2 Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12162648"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5364","DOI":"10.1038\/s41598-020-62298-z","article-title":"Finding Plastic Patches in Coastal Waters using Optical Satellite Data","volume":"10","author":"Biermann","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kikaki, A., Karantzalos, K., Power, C.A., and Raitsos, D.E. (2020). Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea). Remote Sens., 12.","DOI":"10.3390\/rs12111727"},{"key":"ref_19","first-page":"11699","article-title":"Sensing Ocean Plastics with an Airborne Hyperspectral Shortwave Infrared Imager","volume":"52","author":"Garaba","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.marpolbul.2018.08.044","article-title":"Proof of concept for a model of light reflectance of plastics floating on natural waters","volume":"135","author":"Dufaur","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Vicente, V., Clark, J.R., Corradi, P., Aliani, S., Arias, M., Bochow, M., Bonnery, G., Cole, M., C\u00f3zar, A., and Donnelly, R. (2019). Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements. Remote Sens., 11.","DOI":"10.3390\/rs11202443"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2017.11.023","article-title":"An airborne remote sensing case study of synthetic hydrocarbon detection using short wave infrared absorption features identified from marine-harvested macro- and microplastics","volume":"205","author":"Garaba","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"713","DOI":"10.5194\/essd-13-713-2021","article-title":"Hyperspectral-reflectance dataset of dry, wet and submerged marine litter","volume":"13","author":"Knaeps","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-84867-6","article-title":"Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment","volume":"11","author":"Moshtaghi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-74543-6","article-title":"Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: Toward the application of multispectral imagery","volume":"10","author":"Corbari","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Serranti, S., Fiore, L., Bonifazi, G., Takeshima, A., Takeuchi, H., and Kashiwada, S. (2019). Microplastics Characterization by Hyperspectral Imaging in the SWIR Range, SPIE.","DOI":"10.1117\/12.2542793"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Topouzelis, K., Papageorgiou, D., Karagaitanakis, A., Papakonstantinou, A., and Arias Ballesteros, M. (2020). Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019). Remote Sens., 12.","DOI":"10.3390\/rs12122013"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rokni, K., Ahmad, A., Selamat, A., and Hazini, S. (2014). Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sens., 6.","DOI":"10.3390\/rs6054173"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lechthaler, S., Waldschl\u00e4ger, K., Stauch, G., and Sch\u00fcttrumpf, H. (2020). The Way of Macroplastic through the Environment. Environments, 7.","DOI":"10.3390\/environments7100073"},{"key":"ref_30","unstructured":"Sheppard, C. (2019). Chapter 17\u2014Macroplastics Pollution in the Marine Environment. World Seas: An Environmental Evaluation, Academic Press. [2nd ed.]."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e1398","DOI":"10.1002\/wat2.1398","article-title":"Plastic debris in rivers","volume":"7","author":"Schwarz","year":"2020","journal-title":"WIREs Water"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.marpolbul.2019.04.029","article-title":"Sources, transport, and accumulation of different types of plastic litter in aquatic environments: A review study","volume":"143","author":"Schwarz","year":"2019","journal-title":"Mar. Pollut. Bull."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"van Emmerik, T., Kieu-Le, T.-C., Loozen, M., van Oeveren, K., Strady, E., Bui, X.-T., Egger, M., Gasperi, J., Lebreton, L., and Nguyen, P.-D. (2018). A Methodology to Characterize Riverine Macroplastic Emission Into the Ocean. Front. Mar. Sci., 5.","DOI":"10.3389\/fmars.2018.00372"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.scitotenv.2016.05.084","article-title":"Plastic waste in the marine environment: A review of sources, occurrence and effects","volume":"566\u2013567","author":"Li","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, X., Wang, W., Ni, X., Chu, X., Li, Y.-F., and Sun, C. (2018). Evaluation of Near-infrared hyperspectral imaging for detection of peanut and walnut powders in whole wheat flour. Appl. Sci., 8.","DOI":"10.3390\/app8071076"},{"key":"ref_36","first-page":"1","article-title":"Linear discriminant analysis-a brief tutorial","volume":"18","author":"Balakrishnama","year":"1998","journal-title":"Inst. Signal Inf. Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"ref_38","unstructured":"Kim, S.-J., Magnani, A., and Boyd, S. Robust fisher discriminant analysis. Advances in Neural Information Processing Systems, Stanford University."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Garaba, S.P., Arias, M., Corradi, P., Harmel, T., de Vries, R., and Lebreton, L. (2020). Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics. J. Hazard. Mater., 124290.","DOI":"10.1016\/j.jhazmat.2020.124290"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.3390\/s150102205","article-title":"PET and PVC Separation with Hyperspectral Imagery","volume":"15","author":"Moroni","year":"2015","journal-title":"Sensors"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.saa.2018.03.006","article-title":"A hierarchical classification approach for recognition of low-density (LDPE) and high-density polyethylene (HDPE) in mixed plastic waste based on short-wave infrared (SWIR) hyperspectral imaging","volume":"198","author":"Bonifazi","year":"2018","journal-title":"Spectrochim. Acta Part A: Mol. Biomol. Spectrosc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"111176","DOI":"10.1016\/j.rse.2019.04.029","article-title":"High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity","volume":"231","author":"Montes","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1080\/01431169208904049","article-title":"High-spectral resolution data for determining leaf water content","volume":"13","author":"Danson","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"127655","DOI":"10.1016\/j.chemosphere.2020.127655","article-title":"Rapid and direct detection of small microplastics in aquatic samples by a new near infrared hyperspectral imaging (NIR-HSI) method","volume":"260","author":"Piarulli","year":"2020","journal-title":"Chemosphere"},{"key":"ref_45","unstructured":"Eldin, A., and Akyar, I. (2011). Near infra red spectroscopy. Wide Spectra Qual. Control. InTech Rij. Croat., 237\u2013248."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1080\/01431161.2010.520346","article-title":"Correction of cirrus effects in Sentinel-2 type of imagery","volume":"32","author":"Richter","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.isprsjprs.2020.09.009","article-title":"Mapping plastic materials in an urban area: Development of the normalized difference plastic index using WorldView-3 superspectral data","volume":"169","author":"Guo","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jaridenv.2014.09.010","article-title":"Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater","volume":"113","author":"Fu","year":"2015","journal-title":"J. Arid Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Balsi, M., Moroni, M., Chiarabini, V., and Tanda, G. (2021). High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing. Remote Sens., 13.","DOI":"10.3390\/rs13081557"},{"key":"ref_50","unstructured":"Mehrubeoglu, M., Van Sickle, A., and McLauchlan, L. Borrowing least squares analysis from spectral unmixing to classify plastics in SWIR hyperspectral images. Hyperspectral Imaging and Applications, International Society for Optics and Photonics."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hueni, A., and Bertschi, S. (October, January 26). Detection of sub-pixel plastic abundance on water surfaces using airborne imaging spectroscopy. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323556"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1255\/sew.2019.a3","article-title":"Hyperspectral imaging applied to the waste recycling sector","volume":"3","author":"Bonifazi","year":"2019","journal-title":"Spectrosc. Eur."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.chemolab.2019.02.009","article-title":"Improvement of recyclable plastic waste detection\u2014A novel strategy for the construction of rigorous classifiers based on the hyperspectral images","volume":"187","author":"Pieszczek","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:14:15Z","timestamp":1760163255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,15]]},"references-count":53,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122335"],"URL":"https:\/\/doi.org\/10.3390\/rs13122335","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,15]]}}}