Combating the imminent environmental problems associated with plastic litter
requires a synergy of monitoring strategies, clean-up efforts, policymaking
and interdisciplinary scientific research. Lately, remote sensing
technologies have been evolving into a complementary monitoring strategy
that might have future applications in the operational detection and
tracking of plastic litter at repeated intervals covering wide geospatial
areas. We therefore present a dataset of Lambertian-equivalent spectral
reflectance measurements from the ultraviolet (UV, 350 nm) to shortwave
infrared (SWIR, 2500 nm) of synthetic hydrocarbons (plastics). Spectral
reflectance of wet and dry marine-harvested, washed-ashore, and virgin
plastics was measured outdoors with a hyperspectral spectroradiometer.
Samples were harvested from the major accumulation zones in the Atlantic and
Pacific oceans, suggesting a near representation of plastic litter in global
oceans. We determined a representative bulk average spectral reflectance for
the dry marine-harvested microplastics dataset available at 10.21232/jyxq-1m66 (Garaba and Dierssen,
2019c). Similar absorption features were identified in the dry samples of
washed-ashore plastics: dataset available at 10.21232/ex5j-0z25 (Garaba and Dierssen,
2019a). The virgin pellets samples consisted of 11 polymer types
typically found in floating aquatic plastic litter: dataset available at
10.21232/C27H34 (Garaba and
Dierssen, 2017). Magnitude and shape features of the spectral reflectance
collected were also evaluated for two scenarios involving dry and wet
marine-harvested microplastics: dataset available at 10.21232/r7gg-yv83 (Garaba and Dierssen,
2019b). Reflectance of wet marine-harvested microplastics was noted to be
lower in magnitude but had similar spectral shape to that of dry
marine-harvested microplastics. Diagnostic absorption features common in the
marine-harvested microplastics and washed-ashore plastics were identified at
∼931, 1215, 1417 and 1732 nm. In addition, we include metrics
for a subset of the marine-harvested microplastics related to particle
morphology, including sphericity and roundness. These datasets are also
expected to improve and expand the scientific evidence-based knowledge of
optical characteristics of common plastics found in aquatic litter.
Furthermore, these datasets have potential use in radiative transfer
simulations exploring the effects of plastics on ocean colour remote sensing
and developing algorithms applicable to remote detection of floating plastic
litter.
Introduction
The amount of plastic litter in the natural environment is growing
exponentially, and this challenge has led to a huge demand for integrated
and sustainable monitoring strategies (Lebreton et al., 2018; Maximenko et
al., 2016; G20, 2017; Werner et al., 2016). Remote sensing is a widely
considered tool that can provide a complementary avenue of wide geospatial
and spectral information about plastics in natural waters (Maximenko et
al., 2016). Current key requirements expected from remote sensing are to
detect, identify, quantify and track floating plastics. Feasibility studies
centred on these four requirements have shown promising prospects in remote
sensing of floating and submerged litter (Garaba et al., 2018; Aoyama,
2018; Kako et al., 2012; Topouzelis et al., 2019). Although current efforts
are promising, there is a need to advance remote sensing of plastics and
adapt future sensors to generate plastic-related end products. In line with
this, a new generation of satellite missions (e.g. PRISMA – Italian Space
Agency; EnMap – German Aerospace Centre; PACE – National Aeronautics and
Space Administration) is anticipated to advance remote sensing of the
environment through hyperspectral observations from the ultraviolet (UV,
∼350 nm) to shortwave infrared (SWIR, ∼2500 nm). While these future missions are not dedicated to plastic litter
studies, they are likely to provide essential knowledge of high-quality,
hyperspectral and wide geospatial coverage information pertinent to plastics.
Going forward, many satellite missions will be supported by observations
from unmanned aerial systems, aircrafts and high-altitude pseudo-satellites.
A limited number of high-quality hyperspectral measurements of plastic types
found in marine litter have been reported or are in open-access
repositories. We therefore conducted measurements from the UV–SWIR with
the goals of contributing towards (i) creating a high-quality baseline
hyperspectral reflectance dataset of weathered plastics being washed ashore
or floating in the oceans, (ii) identifying absorption features of naturally
weathered plastics, (iii) demonstrating the high reflectivity of plastics
compared to other optically active constituents of the oceans, (iv) creating
an open-access spectral reference library for improved radiative transfer
simulations, and (v) proposing algorithms essential to “detect, identify,
quantify, and track” plastics as unique from other floating debris. We present
the detailed steps that were completed to acquire these measurements of the
virgin and naturally weathered plastics found in marine and land-based
litter.
Methods and materialsSamples
We used a set of specimens consisting of macroplastics (diameter>5mm) and microplastics (diameter<5mm). The
macroplastics were collected during clean-up activities along the western
coastline of the United States of America (USA), now being used to create a public
awareness campaign under the theme “Washed Ashore: Art to Save the Sea”, a travelling
art exhibition in the USA. At the time of the experiment, around midday on 25 March 2015, the exhibit was at the Mystic Aquarium in Connecticut. We believe these
objects (buoys, handles, bottle caps, containers, styrofoam, ropes, toys,
diving fins and nets) had undergone natural weathering at sea and/or on land,
based on careful visual inspection with particular interest in shapes, type
of the original object and colour. The macroplastics had different colour shades
including blue, green, yellow, orange, peach, beige, ivory and white
(Garaba and Dierssen, 2018).
Marine-harvested microplastic samples were obtained from the western North
Atlantic Ocean using a Neuston net (mesh size = 335 µm) in the top
0.25 m seawater layer (Law et al., 2010). After collection with
the nets, the microplastics were left to dry followed by hand separation
before storage in glass scintillation vials at Sea Education Association
archives. Additional marine-harvested microplastic specimens used in this
study were collected from Kamilo Point in Hawaii, USA, by Bill Robberson and
Anna-Marie Cook (Environmental Protection Agency, USA). The Kamilo Point
samples were not sieved as was done for the North Atlantic samples, due to
the quantity that was available; we therefore classified them as aggregated
microplastics. Dry virgin pellets of varying opacity were chosen to
represent the polymer source types that have been identified in specimens
harvested from sediments and aquatic sampling (GESAMP, 2015; Hidalgo-Ruz
et al., 2012). The polymer types considered were polyvinyl chloride (PVC),
polyamide or nylon (PA 6.6 and PA 6), low-density polyethylene (LDPE),
polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS),
fluorinated ethylene propylene teflon (FEP), terpolymer lustran 752 (ABS),
Merlon, and polymethyl methacrylate (PMMA).
The variability in apparent colour and shape of the marine-harvested
microplastics and washed-ashore macroplastics is a plausible representation
of the plastic litter that is being found in the aquatic and terrestrial
environment but may not necessarily represent all the plastic litter found
globally.
Spectral reflectance measurements
Hemispherical directional reflectance measurements (Nicodemus et al., 1977) of all
specimens were conducted outdoors during daylight hours ±3 h
around midday using an Analytical Spectral Devices (ASD)
FieldSpec® 4 hyperspectral spectroradiometer (Malvern
Panalytical, USA) between 350 and 2500 nm. An 8.5∘ foreoptic was
used during the spectral measurements of the dry macroplastics at a
45∘ nadir angle that was 10 cm above target object. A 99 %
Spectralon® Lambertian plaque (Labsphere, USA) was used for
white referencing and optimizing the integration time of the
spectroradiometer. It was assumed that by using a Spectralon®
Lambertian plaque for white referencing we eliminated the effects of varying
setup geometry during measurements. Spectra were recorded first over the
plaque, then from five different spots of each dry macroplastic object and then
over the plaque. A single spectral measurement was derived as an average of
20 continuous automated scans. Microplastics were aggregated on a black neoprene
rubber mat to create an optically dense target before each spectral
measurement (Fig. 1). This black rubber mat was used as
background target because it had a negligible spectral reflectance over the
spectrum range observed. At a 0∘ nadir angle, a 1∘
foreoptic was placed 8 cm above the aggregated microplastics on the black
rubber mat and reference measurements were made using a Spectralon®
Lambertian plaque (Labsphere, USA). Again, here a spectrum was first
collected over the plaque, followed by 10 measurements over the aggregated
dry microplastics and then finally over the plaque. Before each measurement
over the dry microplastics, we gently mixed the particles to rearrange the
orientation and location of the particles in an effort to get the most
representative bulk spectral signal. A similar approach was used to perform
additional spectral measurements of wet microplastics in filtered seawater
with a salinity of 30 ppt. Our selected setup was determined to be optimum
and minimized instrument and user shading.
Experimental setup with the aggregated (a) dry and (b) wet marine-harvested microplastics. Black neoprene rubber was used as a background in
a dark spray-painted container to mitigate background light during spectral
reflectance measurements.
Data processing
Lambertian-equivalent spectral reflectance (R) was calculated as the ratio of
the measurement taken over the sample, divided by that taken over the plaque and
normalized by the spectral calibration of the plaque. Natural outdoor
lighting allowed us to measure spectral reflectance with good
signal-to-noise ratio from the UV to SWIR, with the exception of certain SWIR
regions where the atmosphere is opaque. These regions of the spectrum are
shown as gaps in the continuous spectrum, from 1350 to 1410 nm and 1800 to 1950 nm in the dataset. Average spectra were calculated for each set
of repeated measurements. All data processing, statistics and plots were
generated in MathWorks MATLAB.
Spectral absorption features
In general, the spectral reflectance of an optically active object (e.g. plastic, corals and seawater) has a characteristic shape that explains how it
can reflect or absorb light. The spectral shape is a combination of peak
(reflection or fluorescence) features and trough (absorption) features that
are distinctive optical properties of the objects. An absorption feature
would occur at spectral wavebands where the object absorbs more light and
reflects less light compared to the neighbouring wavebands. Here, a priori knowledge
about typical absorption features in hydrocarbons or plastics was combined
with visual inspection of measured R (Fig. 2a). Further
verification of these identified absorption features was done by applying a
moving-average filter with a window of 19 nm to derive a smoothed R. Second-order derivatives of the smoothed spectra were then computed to generate
derivative R signals with enhanced absorption features (Fig. 2b).
Using derivatives of spectra has been shown to be a robust analytical tool
in remote sensing (Dierssen et al., 2015; Huguenin and Jones, 1986; Russell
et al., 2016; Tsai and Philpot, 1998). Continuum removal was applied to the
R, followed by calculating a relative band-depth index to enumerate the
absorption intensity (Eq. 1). An end and start waveband was
obtained from a MathWorks MATLAB R2016a convhull function. The function
objectively locates the convex hull, i.e. wavebands immediately before
(λ1) and after (λ3) the absorption feature
waveband (Fig. 2c). The equation used to calculate the band depth
(bd) at the central wavelength (λ2) from the reflectance at
the three wavebands is as follows:
bd=R1-R2+λ2-λ1λ3-λ1.
Band-depth indexes are widely used as proxies for detection and
quantification of optically active objects in natural environments
(Clark, 1983, 1999; Dierssen, 2019). Absorption features are thus
enhanced after being normalized by the continuum removal approach
(Fig. 2d). Diagnostic absorption features refer to those parts of
the spectrum unique to a particular object such that they possess a similar
shape and are located at a specific wavelength range (Clark et al., 2003). An inter-comparison to check for
diagnostic absorption features was conducted using the macroplastic and
microplastic spectra.
(a) Example spectral reflectance used for visual inspection to
identify absorption features highlighted by the vertical lines, (b) second
derivative signal validating the location of absorption features, (c) continuum line generated from the convhull function and (d) continuum-removed signal.
The degree of spectral shape likeness among the measured R was calculated
using a quantitative similarity scoring algorithm (Wan et
al., 2002). A spectral contrast angle ranging from (0∘ being a very
strong degree of similarity and 90∘ being no similarity) was
determined after converting the spectra of two samples into a
multi-dimensional vector that is not affected by the inherent intensity of
the spectra but only depends on the shape (Eq. 2). Assuming x
and y to be reflectance at a given wavelength of a sampled and reference
target, the spectral contrast angle (Θ) is derived as follows:
Θ=cos-1Σx⋅yΣx2Σy2.
A scale to evaluate spectral shape similarity classified the results of
Eq. (2) as very strong (0∘≤Θ≤5∘), strong (5∘<Θ≤10∘), moderate (10∘<Θ≤15∘), weak
(15∘<Θ≤20∘) and very weak
(20∘<Θ) (Garaba and Dierssen, 2018).
Microplastic particle morphology
A Marathon electronic digital calliper was used to measure the size
distribution of dry marine-harvested microplastic particles. Additional
particle descriptors included sphericity, roundness and a qualitative
description of colour. Particle sphericity and roundness were determined
according to a qualitative scale (Powers, 1953).
ResultsMacroplastics
Spectral reflectance of the different dry washed-ashore macroplastics (Garaba and Dierssen, 2019a) had significant differences in
the visible spectrum related to the intrinsic colour of each object
(Fig. 3). Blue objects peaked around 450 nm, green objects around
550 nm, while white objects had a flatter reflectance in visible wavelengths.
Beige and ivory coloured objects had rapidly increasing reflectance in the
visible with an 8-fold reflectance magnitude rise from 400 to 700 nm.
Yellow, peach and orange objects also had increasing reflectance in the
visible but not as pronounced as in the beige and ivory objects, ranging
from a 3- to 4-fold increase in reflectance. Overall, the highest
reflectance was noted on the beige object: R=0.88 around 850 nm. For all
the objects, the reflectance peaked in the near-infrared (NIR), followed by a general
decrease in the SWIR with several absorption features resulting in localized
dips and peaks. Despite the variations in the spectral magnitude and shape,
absorption features common to all the macroplastics were located at
wavebands centred close to 931, 1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm (Fig. 3). The location of the absorption
features was validated and confirmed by derivative analyses of each
respective spectrum.
Spectral reflectance of dry washed-ashore macroplastics harvested
along the western coast of the USA. Absorption features noted in
marine-harvested microplastics are highlighted by the vertical lines
(centred around ∼931, 1045, 1215, 1417, 1537,
1732, 2046 and 2313 nm).
Marine-harvested microplastics
Spectral reflectance of the dry marine-harvested microplastics (Garaba and Dierssen, 2019c) increased with wavelength
reaching the highest values in the NIR at 850 nm then decreasing towards the SWIR
wavebands. All spectra were close to uniform in both spectral shape (mean
Θ<5∘) and magnitude (percentage ranges
<40 %) compared to the macroplastics. A non-parametric
Kruskal–Wallis one-way analysis of variance was utilized to determine if any
differences existed in the measured spectra of the dry marine-harvested
microplastics. The statistical test suggested no significant differences
(p<0.05) in the spectral reflectance from 350 to 2500 nm. We
therefore determined a representative dry marine-harvested microplastic
spectral endmember (Fig. 4). Absorption features identified in
the dry washed-ashore macroplastic specimens (Fig. 3) were also
found in the dry marine-harvested microplastics (Fig. 4).
Endmember spectral reflectance of the dry marine-harvested microplastics ± standard deviation (dashed lines). Identified
unique absorption features are highlighted by the vertical lines and provide the
wavebands that are outlined in grey (centred around ∼931,
1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm).
Wet marine-harvested microplastics (Garaba and Dierssen,
2019b) had similar absorption features as found in the dry marine-harvested
specimens (Fig. 5). However, the magnitude of R decreased by 12 % in the UV to 90 % in the SWIR, due to the presence of water mixed
with the samples. The loss of reflectance in the plastics was observed to be
consistent with the increase in the absorption coefficient of pure water
(Fig. 5b). Average decrease in the measured R was 56±23 %. In addition, the spectral absorption features were less pronounced in
the wet samples compared to the dry and some were not noticeable (1045,
1537 and all >2000)
(a) Mean bulk spectral reflectance of dry and wet marine-harvested
microplastics ± standard deviation (dashed lines).
Absorption features noted in marine-harvested microplastics are highlighted
by the vertical lines (centred around ∼931, 1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm). (b) Absorption coefficient
of pure water (Rottgers et al., 2011).
Spectral reflectance of dry virgin pellets and absorption features
found in marine-harvested and washed-ashore plastics highlighted by the
vertical lines. Absorption features identified in marine-harvested
microplastics are highlighted by the vertical lines (centred around
∼931, 1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm).
Virgin pellets
Spectral properties of the dry virgin pellets (Garaba and
Dierssen, 2017) varied in magnitude and shape (Fig. 6). However,
two specimens of PA (6 and 6.6) did show very strong similarities (Θ=2.1∘), although the apparent opacity of PA 6 was lower than
that of PA 6.6. FEP was generally flat in the NIR to SWIR. Overall, the
highest reflectance was observed in the specimens of ABS, PMMA and PVC, while
the lowest was observed in PET. Only FEP and PVC were noted to have a strong
reflectance in the SWIR>1900 nm with R>0.4. Several
of the absorption features from the marine-harvested and washed-ashore
specimens were duplicated in the dry virgin pellets, although some features
were absent, e.g. FEP, or shifted compared to the marine plastics
(Fig. 6). We also determined that our marine-harvested
microplastic endmember was best matched to PMMA, PP, LDPE and PET (Garaba and Dierssen, 2018).
Microplastic particle side distribution and colour.
Morphometric measurements were completed on a total of 47 microplastic
particles from different size classes (Table 1). The particles were
brittle and could fracture with handling. Sphericity of the observed
particles ranged from low to high sphericity, whilst the roundness was
between subangular to very angular (Table 1). Dry virgin pellets in
common ocean plastic litter had varying opacity of the colour white.
Table 1 is available as an Excel sheet in the Supplement.
Discussion
We measured Lambertian-equivalent spectral reflectances of washed-ashore,
marine-harvested, and virgin plastics and identified eight absorption
features (centred around ∼931, 1045, 1215, 1417,
1537, 1732, 2046 and 2313 nm) in most of the weathered specimen.
Location of these absorption features agreed well with prior reports
(Asadzadeh and de Souza Filho, 2017; Hörig et al., 2001). Of these eight
wavebands, we concluded that ∼931, 1215, 1417 and 1732 nm were diagnostic absorption features after continuum removal and
derivative analyses. Several studies have already shown prospective
applications of the ∼1215 and ∼1732 nm
wavebands in detection and quantification algorithms for floating and
land-based plastic litter (Garaba et al., 2018; Goddijn-Murphy and Dufaur,
2018; Kühn et al., 2004). Unfortunately, the 931 nm and the 1417 nm
absorption features fall outside the atmospheric window. These features pose
a challenge for algorithm development as the plastic-specific signal will be
scrambled in the signal from atmospheric gases, especially water vapour
around ∼900 and ∼1400 nm. We also simulated
the potential detection of submerged plastics and observed a decrease in the
measured R, which was consistent with the enhanced absorption of light by pure
water in the SWIR (Fig. 5). One aspect that was not addressed
with the dataset was the depth at which the submerged plastic can be
detected, it is important to further study the optical properties of plastic
litter with varying water depth. Due to enhanced water absorption, spectral
features in the NIR and SWIR region will quickly disappear as particles
submerge, and only reflectance in visible wavelengths would be detectable
with remote sensing.
The polymer characterization of the specimen is a key factor in advancing
scientific evidence-based knowledge, complemented by spectral measurements as
it enables researchers to further create essential descriptors for remote
sensing applications related to plastic litter. Laboratory techniques are
typically used to accurately determine polymer compositions of
marine-harvested or washed-ashore plastics, these include Fourier transform
infrared (FTIR), Raman spectroscopy and pyrolysis gas chromatography (Thevenon et al., 2014). However, in our case, the washed-ashore
macroplastics were part of an ongoing travelling plastic litter awareness
exhibit and no detailed analyses could be conducted to determine polymer
composition. The only descriptors obtained from the washed-ashore
macroplastics were the object colour, shape and form, and spectral reflectance.
Future campaigns are recommended to collect additional high-quality
descriptors (polymer composition, refractive index, date of manufacture,
sphericity and individual size distribution) of plastic specimens that will
improve classification and radiative transfer modelling efforts. It is also
important to consider the possibilities of expanding the spectral reference
library through spectral unmixing simulations to create blended polymers
from the virgin pellets.
Variability in the spectra was reported within 1 standard deviation and a
median was also determined to be consistent with the literature (Dierssen,
2019; Russell et al., 2016; Zibordi et al., 2011). However, future
measurements should include comprehensive uncertainty budgets to enable
advanced error propagation efforts when data are assimilated into radiative transfer models. Implementing the algorithms that use spectral shape and
continuum removal approaches reduces the uncertainties related to variations
in magnitude. These investigations should also explore the possible
anisotropic optical properties of plastic litter, especially as it breaks
down and weathers in the natural environment.
Data availability
Quality control was performed according to the guidelines of SeaDataNet. All the datasets are in open-access via the online repository EcoSIS spectral library https://ecosis.org/ (last access: 7 January 2020; EcoSIS, 2020). The dry marine-harvested
microplastics spectral data are available at 10.21232/jyxq-1m66 (Garaba and Dierssen,
2019c). Washed-ashore plastics spectral data are available at 10.21232/ex5j-0z25 (Garaba and Dierssen,
2019a). The virgin pellets spectral data are available at 10.21232/C27H34 (Garaba and Dierssen,
2017). The dry and wet marine-harvested microplastics spectral dataset is available
at 10.21232/r7gg-yv83 (Garaba and Dierssen, 2019b).
Conclusions and outlook
We have established an open-access dataset of hyperspectral reflectances of
dry washed-ashore macroplastics, dry marine-harvested microplastics,
artificially wetted marine-harvested microplastics and virgin pellets. The
dataset provides some of the first baseline measurements that can be
assimilated into radiative transfer modelling to improve scientific
knowledge of the contribution of plastic litter to the bulk signal reaching
remote sensing sensors. Furthermore, such knowledge about the hyperspectral
characteristics of micro and macroplastics litter can be used to evaluate
the capabilities and application of current multispectral sensors in remote
sensing efforts. Using spectral response functions of current remote sensing
tools (airborne, unmanned aerial systems, high-altitude pseudo-satellites,
satellites) it is also possible to simulate the spectral signature of our
dataset, this information would be crucial in the design of future or
planned remote sensing tools.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-12-77-2020-supplement.
Author contributions
SPG and HMD contributed equally to the experiment and manuscript
preparation.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank Brandon J. Russell, Adam Chlus and Kaylan Randolph for their support during spectral data collection. We extend our gratitude to Anna-Marie Cook and Bill Robberson at the US EPA Region 9 Marine Debris Program, Kara L. Law, Jessica Donohue at the Sea Education Association, the Institute of Materials Science at the University of Connecticut, and Mystic Aquarium for providing plastic samples.
Financial support
This research has been supported by the National Aeronautics and Space Administration Ocean Biology and Biogeochemistry (grant no. NNX15AC32G) and the Deutsche Forschungsgemeinschaft (grant no. 417276871).
Review statement
This paper was edited by Jens Klump and reviewed by Elizabeth C. Atwood and one anonymous referee.
ReferencesAoyama, T.: Extraction of marine debris in the Sea of Japan using satellite
images, in Proceedings of SPIE Volume: 10778, Remote sensing of the open and
coastal ocean and inland waters, Honolulu, Hawaii, USA, 2018,
107780R-107781–107780R-107788, 10.1117/12.2324621, 2018.Asadzadeh, S. and de Souza Filho, C. R.: Spectral remote sensing for
onshore seepage characterization: A critical overview, Earth-Sci. Rev., 168,
48–72, 10.1016/j.earscirev.2017.03.004, 2017.Clark, R. N.: Spectral properties of mixtures of montmorillonite and dark
carbon grains: Implications for remote sensing minerals containing
chemically and physically adsorbed water, J. Geophys. Res.-Solid Earth, 88,
10635–10644, 10.1029/JB088iB12p10635, 1983.
Clark, R. N.: Spectroscopy of rocks and minerals, and principles of
spectroscopy, in: Manual of Remote Sensing, edited by: Rencz, A. N., John
Wiley and Sons, New York, 3-58, 1999.Clark, R. N., Swayze, G. A., Livo, K. E., Kokaly, R. F., Sutley, S. J.,
Dalton, J. B., McDougal, R. R., and Gent, C. A.: Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems,
J. Geophys. Res.-Planets, 108, 5131, 10.1029/2002JE001847, 2003.Dierssen, H., McManus, G. B., Chlus, A., Qiu, D., Gao, B.-C., and Lin, S.:
Space station image captures a red tide ciliate bloom at high spectral and
spatial resolution, P. Natl. Acad. Sci. USA, 112, 14783–14787,
10.1073/pnas.1512538112, 2015.Dierssen, H. M.: Hyperspectral Measurements, Parameterizations, and
Atmospheric Correction of Whitecaps and Foam From Visible to Shortwave
Infrared for Ocean Color Remote Sensing, 7, 1–18,
10.3389/feart.2019.00014, 2019.EcoSIS: Ecosystem Spectral Information System, Online Repository, available at: https://ecosis.org/, last access: 7 January 2020.
G20: Annex to G20 Leaders Declaration: G20 Action Plan on Marine Litter, G20
Summit 2017, Hamburg, Germany (7–8 July), 2017.Garaba, S. P. and Dierssen, H. M.: Spectral reference library of 11 types
of virgin plastic pellets common in marine plastic debris, Ecological
Spectral Information System (EcoSIS), 10.21232/C27H34, 2017.Garaba, S. P. and Dierssen, H. M.: An airborne remote sensing case study of
synthetic hydrocarbon detection using short wave infrared absorption
features identified from marine-harvested macro- and microplastics, Remote
Sens. Environ., 205, 224–235, 10.1016/j.rse.2017.11.023, 2018.Garaba, S. P. and Dierssen, H. M.: Spectral reflectance of washed ashore
macroplastics, Ecological Spectral Information System (EcoSIS),
10.21232/ex5j-0z25, 2019a.Garaba, S. P. and Dierssen, H. M.: Spectral reflectance of dry and wet
marine-harvested microplastics from Kamilo Point, Pacific Ocean, Ecological
Spectral Information System (EcoSIS), 10.21232/r7gg-yv83, 2019b.Garaba, S. P. and Dierssen, H. M.: Spectral reflectance of dry
marine-harvested microplastics from North Atlantic and Pacific Ocean,
Ecological Spectral Information System (EcoSIS), 10.21232/jyxq-1m66,
2019c.Garaba, S. P., Aitken, J., Slat, B., Dierssen, H. M., Lebreton, L.,
Zielinski, O., and Reisser, J.: Sensing ocean plastics with an airborne
hyperspectral shortwave infrared imager, Environ. Sci. Technol., 52,
11699–11707, 10.1021/acs.est.8b02855, 2018.
GESAMP: Sources, fate and effects of microplastics in the marine
environment: a global assessment.
(IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP/UNDP Joint Group of Experts on
the Scientific Aspects of Marine Environmental Protection). GESAMP Report
and Studies No. 90, International Maritime Organization – London, UK, 96,
2015.Goddijn-Murphy, L. and Dufaur, J.: Proof of concept for a model of light
reflectance of plastics floating on natural waters, Mar. Pollut. Bull., 135,
1145–1157, 10.1016/j.marpolbul.2018.08.044, 2018.Hidalgo-Ruz, V., Gutow, L., Thompson, R. C., and Thiel, M.: Microplastics in
the Marine Environment: A Review of the Methods Used for Identification and
Quantification, Environ. Sci. Technol., 46, 3060–3075,
10.1021/es2031505, 2012.Hörig, B., Kühn, F., Oschütz, F., and Lehmann, F.: HyMap
hyperspectral remote sensing to detect hydrocarbons, Int. J. Remote Sens.,
22, 1413–1422, 10.1080/01431160120909, 2001.Huguenin, R. L. and Jones, J. L.: Intelligent information extraction from
reflectance spectra: Absorption band positions, J. Geophys. Res.-Solid
Earth, 91, 9585–9598, 10.1029/JB091iB09p09585, 1986.Kako, S. I., Isobe, A., and Magome, S.: 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, 1156–1162,
10.1016/j.marpolbul.2012.03.024, 2012.Kühn, F., Oppermann, K., and Hörig, B.: Hydrocarbon Index – an
algorithm for hyperspectral detection of hydrocarbons, Int. J. Remote Sens.,
25, 2467–2473, 10.1080/01431160310001642287, 2004.Law, K. L., Morét-Ferguson, S., Maximenko, N. A., Proskurowski, G.,
Peacock, E. E., Hafner, J., and Reddy, C. M.: Plastic accumulation in the
North Atlantic Subtropical Gyre, Science, 329, 1185–1188,
10.1126/science.1192321, 2010.Lebreton, L., Slat, B., Ferrari, F., Sainte-Rose, B., Aitken, J., Marthouse,
R., Hajbane, S., Cunsolo, S., Schwarz, A., Levivier, A., Noble, K.,
Debeljak, P., Maral, H., Schoeneich-Argent, R., Brambini, R., and Reisser,
J.: Evidence that the Great Pacific Garbage Patch is rapidly accumulating
plastic, Sci. Rep., 8, 4666, 10.1038/s41598-018-22939-w, 2018.
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.: 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, 2016.
Nicodemus, F. E., Richmond, J. C., Hsia, J. J., Ginsberg, I. W., and
Limperis, T.: Geometrical considerations and nomenclature for reflectance, Final Report National Bureau of Standards, US Department of Commerce, National Bureau of Standards, Washington, DC, USA, 1–52, NBS MN-160, 1977.Powers, M. C.: A new roundness scale
for sedimentary particles, J. Sediment.
Res., 23, 117–119, 10.1306/d4269567-2b26-11d7-8648000102c1865d, 1953.
Rottgers, R., Doerffer, R., McKee, D., and Schonfeld, W.: Algorithm
Theoretical Basis Document: The Water Optical Properties Processor (WOPP) –
Pure water spectral absorption, scattering, and real part of refractive
index model, Helmholtz-Zentrum Geesthacht and University of Strathclyde,
ESA/ESRIN, 20, 2011.Russell, B., Dierssen, H., LaJeunesse, T., Hoadley, K., Warner, M., Kemp,
D., and Bateman, T.: Spectral reflectance of Palauan reef-building coral
with different symbionts in response to elevated temperature, Remote Sens.
(Basel), 8, 164, 10.3390/rs8030164, 2016.
Thevenon, F., Carroll, C., Sousa, J., and (Editors): Plastic debris in the
ocean: The characterization of marine plastics and their environmental
impacts, situation analysis report, International Union for Conservation of
Nature, Gland, Switzerland, 52, 2014.Topouzelis, K., Papakonstantinou, A., and Garaba, S. P.: Detection of
floating plastics from satellite and unmanned aerial systems (Plastic Litter
Project 2018), Int. J. Appl. Earth Obs. Geoinfo., 79, 175–183,
10.1016/j.jag.2019.03.011, 2019.Tsai, F. and Philpot, W.: Derivative analysis of hyperspectral data, Remote
Sens. Environ., 66, 41–51, 10.1016/S0034-4257(98)00032-7, 1998.Wan, K. X., Vidavsky, I., and Gross, M. L.: Comparing similar spectra: from
similarity index to spectral contrast angle, J. Am. Soc. Mass. Spectrom.,
13, 85–88, 10.1016/S1044-0305(01)00327-0, 2002.
Werner, S., Budziak, A., van Franeker, J., Galgani, F., Hanke, G., Maes, T.,
Matiddi, M., Nilsson, P., Oosterbaan, L., Priestland, E., Thompson, R.,
Veiga, J., and Vlachogianni, T.: Harm caused by Marine Litter, Luxembourg:
Publications Office of the European Union, 92, 2016.Zibordi, G., Berthon, J. F., Mélin, F., and D'Alimonte, D.: Cross-site
consistent in situ measurements for satellite ocean color applications: The
BiOMaP radiometric dataset, Remote Sens. Environ., 115, 2104–2115,
10.1016/j.rse.2011.04.013, 2011.