# Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics

Shungudzemwoyo P Garaba, Manuel Arias, Paolo Corradi, Tristan Harmel, Robin de Vries, Laurent Lebreton

► To cite this version:

Shungudzemwoyo P Garaba, Manuel Arias, Paolo Corradi, Tristan Harmel, Robin de Vries, et al.. Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics. Journal of Hazardous Materials, 2021, 406, pp.124290. 10.1016/j.jhazmat.2020.124290. hal-04890598

HAL Id: hal-04890598

https://ut3-toulouseinp.hal.science/hal-04890598v1

Submitted on 16 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Research paper

# Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics

Shungudzemwoyo P. Garaba<sup>a, b</sup>, Manuel Arias<sup>c</sup>, Paolo Corradi<sup>d</sup>, Tristan Harmel<sup>e</sup>, Robin de Vries<sup>a</sup>, Laurent Lebreton<sup>a, f</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

- Microplastic

- Macroplastic

- Hyperspectral

- Ocean-harvested plastics

- Virgin plastics

- Pixel concentrations

- Reflectance anisotropy

- Radiative transfer equations

## ABSTRACT

We present reflectance measurements collected from virgin and ocean-harvested plastics. Virgin plastics included high and low density polyethylene (HDPE, LDPE), polypropylene (PP) as well as polystyrene (PS). Ocean-harvested plastics were ropes, sheets, foam, pellets and fragmented items previously trawled from the North Pacific Garbage Patch. Nadir viewing angles and plastic pixel coverage were varied to advance our understanding of how reflectance shape and magnitude can be influenced by these parameters. We also investigated the effect of apparent colour of plastics on the measured reflectance from the ultraviolet (UV - 350 nm), visible, near to shortwave infrared (NIR, SWIR - 2500 nm). Statistical analyses indicated that the spectral reflectance of the plastics was significantly correlated to the percentage pixel coverage. There was no clear relationship between the reflectance observed and the viewing nadir angle but dampened materials seemed to be more isotropic (near-Lambertian) than their dry counterparts. A loss in reflectance was also determined between dry and wet plastics. Location of absorption features was not affected by the apparent colour of objects. In general, ocean-harvested plastics shared more identical absorption features (~960, 1215, 1440, 1732, 1920 nm) and had lower reflectance intensity compared to the virgin plastics (~980 nm). Prospects for satellite retrieval of plastic type and pixel plastic coverage are discussed based on Top-of-Atmosphere (TOA) signal simulated through radiative transfer computation using the documented plastic reflectances. Non-linear relationships between TOA reflectance and plastic coverage were observed depending on wavelength and plastic type. Most of the plastics analysed impact significantly the TOA signal but two plastic types did not produce strong signal at TOA (hard fragments, LDPE). Nevertheless, all plastic types produced detectable signals when observations were simulated within the sunglint direction. The measurements collected in this study are an extension to available high quality spectral reference libraries and can support further research in developing remote sensing algorithms for marine litter.

## 1. Introduction

Natural elements of the blue planet include essential ocean variables (EOVs) such as water, coloured dissolved organic material, suspended particulate material and algae (Forget et al., 1999). These natural components are optically active, possessing a unique spectral signature that can be remotely detected by optical sensors. In the last few decades,

scientific evidence-based research combined with numerical simulations have been suggesting an exponential surge in floating ocean plastic litter accumulation (Lebreton et al., 2018; Maximenko et al., 2018). A large fraction of this ocean plastic litter is composed of polyethylene (PE), polypropylene (PP), polystyrene (PS), polyamide (PA) as well as blended materials (Andrady, 2011; Cózar et al., 2014; Lebreton et al., 2018; Suaria et al., 2016). Plastics or synthetic hydrocarbons also possess an

<footnote><sup>a</sup> The Ocean Cleanup, Batavierenstraat 15, 3014 JH Rotterdam, The Netherlands</footnote>

<footnote><sup>b</sup> Marine Sensor Systems Group, Institute for Chemistry and Biology of the Marine Environment, Carl von Ossietzky University of Oldenburg, Schleusenstraße 1, 26382 Wilhelmshaven, Germany</footnote>

<footnote><sup>c</sup> ARGANS Limited FR, 260 Route du Pin Montard - BP234, 06904 Sophia-Antipolis cedex, France</footnote>

<footnote><sup>d</sup> Optics Section, Mechatronics and Optics Division, European Space Research and Technology Centre (ESTEC), European Space Agency, Keplerlaan 1, 2200 AG Noordwijk, The Netherlands</footnote>

<footnote><sup>e</sup> Géosciences Environnement Toulouse (GET), UMR5563, Institut de Recherche pour le Développement (IRD)/Centre National de la Recherche Scientifique (CNRS)/ Université Toulouse 3, 14 Avenue Edouard Belin, 31400 Toulouse, France</footnote>

<footnote><sup>f</sup> The Modelling House Limited, 3 Bay View Road, Raglan 3225, New Zealand</footnote>
optical signature which can be remotely sensed and therefore, can
contribute to the bulk signal reaching an optical sensor in a target pixel.

Ocean colour remote sensing is a key monitoring strategy that gen-
erates proxy information about EOVs (Maximenko et al., 2019; van
Sebille et al., 2020). More recently, in the advent of increasing con-
centrations of floating plastic litter, remote sensing technologies are
being considered as a good complementary source for field observations.
The information can either be derived from indirect observations of
EOVs to infer distributions of plastic litter or direct measurements using
sensors that can detect and identify plastic litter (Mace, 2012; Martí-
nez-Vicente et al., 2019; Maximenko et al., 2019; Ryabinin et al., 2019).
Developing and evaluating technologies for direct monitoring of plastic
litter requires fundamental research about the optical properties of
plastics as well as other bright targets like bubbles from breaking waves,
whitecaps, sea foam or flotsam (Dierssen and Garaba, 2020). Of course,
a comprehensive assessment of current remote sensing tools will also
highlight gaps that can be mitigated by future sensor technologies to
further drive additional research efforts. Not to mention, advancing
these fundamental approaches to operational remote sensing will
further benefit the blue planet and economy (Smail et al., 2019).
Furthermore, remote sensing of marine litter in general is acknowledged
as a tool to better understand oceanographic physical processes that are
sometimes poorly understood but play an important role in ocean dy-
namics, like microscale transport, wind-ocean interactions and coastal
processes. Prospective detection of marine litter is thus expected to help
advance scientific knowledge in these disciplines of study and better
support modelling efforts (van Sebille et al., 2020).

Already, fundamental experiments investigating the spectral char-
acteristics of virgin and naturally weathered plastics have been con-
ducted using hyperspectral in-situ sensors in the ultraviolet (UV) to
shortwave infrared spectrum (SWIR). Key studies that have gathered in-
situ spectral reflectance have reported the identification of diagnostic
absorption features and development of detection algorithms using
washed ashore as well as ocean-harvested plastics (Garaba and Dierssen,
2018). Additionally, the effects of estimated pixel concentration and
physical properties of new or virgin plastics with respects to measured
spectral reflectance have been reported in literature (Goddijn-Murphy
and Dufaur, 2018). However, the concentrations effects have been
researched at limited pixel coverages estimated as ~ (25, 50, 75, 100%)
using new plastics viewed at a 0° nadir angle, providing some finite
insights for practical monitoring applications. Furthermore, the di-
versity in apparent colour of plastic litter is large but this characteristic
has not been well reported with respects to how it affects the spectral
reflectances outside the visible spectrum from the UV (<400 nm), near
infrared (700–1000 nm) to SWIR (1000–2500 nm). The diversity is a
consequence of the additives materials different plastic manufacturers
mix together with virgin polymers to enhance physiochemical proper-
ties of the plastic end-products.

In an effort to go a step further and improve knowledge gained from
these prior studies, we present hemispherical reflectance anisotropy of
dry and wet virgin and ocean-harvested plastic. We further explored the
relationship between target/pixel concentration size and the reflectance
bidirectional properties of the plastic specimens to better simulate
detection limits of floating and washed ashore specimen. Here, we
accurately determined the target concentration using a robust algorithm
that transformed RGB imagery into percentage coverage of plastics in
the field-of-view of the spectroradiometer fore optic. These hyper-
spectral reflectance measurements were conducted in the UV to SWIR
covering wavebands found in sensors used in ocean colour remote
sensing. Prospective application of the dataset established here were
also assessed in terms of satellite signal or Top-of-Atmosphere (TOA)
reflectance through radiative transfer simulations relevant to remote
sensing of plastic litter. Such simulations help to address specific stra-
tegies, estimate minimal signal-to-noise ratio (SNR), optimal sensor
viewing angles or prerequisite wavebands and spectral regions of
interest.

## 2. Methods and material

2.1. Plastic samples

A metal tank with diameter of 0.20 m was spray painted black inside and outside to create a background with minimal to negligible reflec-tance (Fig. 1). Wet samples were left in artificial seawater prepared from a reef salt solution, 35 g of common salt per litre of water, overnight before spectral reflectance measurements. During the course of the non-dry experiments, samples were kept wet by using a spray bottle also making it possible to simulate sea spray. High- and low-density poly-ethylene (HDPE, LDPE), polypropylene (PP) as well as polystyrene (PS) made up the sample set of virgin plastics (Fig. 1). These polymers were presumed a reasonable subset representative of the plastic specimens collected in the eastern North Pacific subtropical waters. Samples collected at sea were transported and stored in laboratory at a temper-ature < 2 °C (Lebreton et al., 2018), to mitigate further degradation or alteration since we observed signs of biofouling and visible surfactants. The ocean-harvested items included ropes, hard fragments, sheets, foam and preproduction pellets (Fig. 1). Each sample was subdivided into predetermined pixel concentrations of 0.01, 0.1, 1, 10, 20, 40, 60, 80 and 100%. This choice was made to get eventually an estimation of what orders of magnitude, in terms of concentration, would be feasible for detection in remote sensing applications and for each sample under consideration. Reflectances of dry ocean-harvested samples were only measured at nadir, all other measurements were completed in artificial seawater to simulate ocean conditions. Prior to the measurements, samples were submersed in the prepared artificial seawater for at least 24 h.

2.2. Spectral measurements

Hemispherical directional reflectances of the HDPE, LDPE, PP and PS
samples were collected outdoors under ambient sunlight at variable sky
conditions around midday. Measurements were completed using a
Malvern Panalytical - Analytical Spectral Device (ASD) FieldSpec 4
spectroradiometer from the UV(350 nm) to SWIR (2500 nm) spectrum.
Each spectral measurement was an average of 20 scans and for each
specimen 10 measurements were collected at each viewing angle and
concentration. We gently mixed the samples in an attempt to capture the
different geometric positions of the samples. Software output data was
interpolated to 1 nm spectral resolution. Due to strong winds, rainfall
and variable cloud coverage, the rest of the experiments were completed
indoors using four Philips EcoClassic 105 W E27 230 V A55 1CT/15 SRP
Tungsten Halogen lights in a Bresser SS-19 softbox to provide diffuse
lighting. The ASD foreoptic detector end was attached to a customised
frame at 1.05 m from target. Our frame was designed in the form of a
goniometer allowing variations in nadir viewing angles, we chose
(θ = 0, 15, 30, 45°) for brevity (Fig. 2).

Background surfaces and wind shields were spray painted black to
mitigate surrounding surfaces reflected glitter or stray light (Fig. 2b).
This background material had very low reflectance which was used for a
baseline (background noise, blank) correction in computing re-
flectances. An 8° fore optic was fitted to the fibre optic detector end of
the spectroradiometer. White referencing was performed using a
5 × 5 in. 99% Spectralon® diffuse Lambertian reflectance panel. By
white referencing, we assume the Spectralon® diffuse panel is the
brightest target that is then used for normalisation to determine the
relative reflectance (dimensionless) of any other target sampled, in our
case plastics. An azimuthal angle (~90°) relative to the light source was
chosen and maintained to mitigate self-shading. Before each reflectance
measurement (R<sub>total</sub>), a white reference measurement was made fol-
lowed by a background measurement (R<sub>background</sub>) of the Krylon® black
spray-painted water tank with and without water. True colour RGB
pictures were captured with a Nikon Coolpix AW130 camera attached
next to the ASD foreoptic at each viewing angle for every sample.
<figure><img src="image_4.png" /><figcaption>Fig. 1. (a) Virgin samples of high and low density polyethylene (HDPE, LDPE), polypropylene (PP), polystyrene (PS) representative of polymers common in ocean floating plastic litter and (b) rope, sheets, foam, pellets and fragmented items harvested from the North Pacific Garbage Patch between 27 July and 19 September 2015 (Lebreton et al., 2018).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. (a) Schematic of experimental setup with frame and (b) custom-made aluminium frame with a goniometer to determine nadir viewing angles. Frame was painted black to mitigate surface reflected glint.</figcaption></figure>

## 2.3. Data analyses

### 2.3.1. Pixel coverage

The perceived region of interest was a circle with radius (r) expressed in metres as a function of sensor height (h) and angle of observation. For nadir position θ = 0° the radius was calculated using r₀ = h tan(α/2), where α = 8° was the field of view of the fore optic. The semi-major axis of the projected ellipse on the horizontal plane created from tilting the

sensor was determined by using r<sub>B</sub> = r<sub>0</sub> / (cos(θ)+tan(α)sin(θ)), where r<sub>0</sub> was the radius at nadir position and θ was the nadir angle. After calculating the area of interest, the pixel size coverage of the plastic specimen was derived from the true colour RGB images using QGIS 3.2 Bonn and Python 3.6 (Fig. 3). Dark, well aggregated and large pieces of plastics were manually digitised in QGIS. Images of the small sized samples were automatically digitised using a brightness-based masking function in Python. Matching/clipping the calculated field of view area
<figure><img src="image_6.png" /><figcaption>Fig. 3. Pixel coverage was derived from (a) an RGB image, (b) automatically or manually digitised, (c) sampling area of spectroradiometer determined and (d) clipping sampling area and calculating actual pixel coverage by plastics.</figcaption></figure>

of our spectroradiometer at the different viewing angles to the RGB imagery was completed to make sure the bulk signal calculated matched the correct region of interest and the accurate percentage pixel concentration.

The spectroradiometer at a height of 1.05 m with an 8° foreoptic had a field-of-view diameter of 0.147 m translating to an area of 0.017 m². Because the target area was a circle at θ = 0° and an ellipse at greater viewing angles (here for θ ≥ 15°), we accounted for pixel coverage changes due to the increase of the semi-major axis of the projected ellipse on the horizontal plane created from tilting the sensor. Calculation of the percentage pixel coverages was performed to improve the accuracy of our predetermined plastic pixel coverages (0.01, 0.1, 1, 10, 20, 40, 60, 80, 100%). Here one of our goals was to better simulate signal detection limits as well as assess any correlations between signal and percentage pixel coverage.

### 2.3.2. Bulk reflectance of plastics

Pixel coverage by plastics was varied between 0.01% ($f_{plastic} = 0.0001$) to 100% ($f_{plastic} = 1$), note we applied the re-calculated values (supplementary material Table S1). To derive the plastic spectral reflectance ($R_{plastic}$) from the total bulk reflectance ($R_{total}$) reaching the sensor we used Eq. (1).

$$R_{plastic} = R_{total} - (1 - f_{plastic}) \times R_{background} \quad (1)$$

A splice correction was implemented to remove or compensate for the steps in spectra due to overlaps by different detectors VNIR ~ (350–1000 nm), SWIR-1 ~ (1000–1800 nm), SWIR-2 ~ (1800–2500 nm) It was completed by adjusting the VNIR and SWIR-2 data to match the SWIR-1 data. The difference at 1000 nm and 1001 nm was used to correct the VNIR data whilst the difference at 1800 nm and 1801 nm was used to correct the SWIR-2 data. Savitzky-Golay least-square smoothing was applied with a third degree polynomial. Note that gaps in some of the measured spectra correspond to wavelength ranges affected by strong absorption of atmospheric gases ~1400 and 1800 nm. Detailed analyses were concentrated on the diagnostic wavebands of plastics 850 nm, 1215 nm and 1732 nm (Garaba and Dierssen, 2020; Goddijn-Murphy and Dufaur, 2018).

### 2.3.3. Spectral shape similarity

Non-parametric Spearman rank correlation test was applied to determine the degree of association between selected variables, represented by correlation coefficient ($ρ$), p-value ($p-val$) and number of variables ($N$). A $p-val$ < 0.05 indicated statistical significance. Spectral contrast angle (SCA) mapping was applied to determine goodness-of-similarity with respects to shape of different samples (Garaba and Dierssen, 2018). An SCA close to zero indicated very strong spectral shape similarity whilst larger angles close to 90° suggested a lack of resemblance in spectra. SCA is a robust technique useful in spectral analyses and is well documented in literature (Garaba and Dierssen, 2018, 2020; Wan et al., 2002). SCA was restricted to the wavelength

ranges relevant to the detection and distinguishing of plastics 850–2500 nm without considering the apparent colour of the object in the visible spectrum (400-700 nm). Processing of the spectral data was done in Matworks Matlab R2016a.

### 2.3.4. Top-of-atmosphere radiative transfer simulations

The Bottom-of-Atmosphere reflectance ($ρ_{BOA}$) was modelled by assuming a mixture of plastic patches surrounded by free water areas (Koeppke, 1984)

$$ρ_{BOA}(θ_s, θ_v, Δφ, λ) = ρ_{plastic}(λ) + (1 - f_{plastic})ρ_{spec}(θ_s, θ_v, Δφ, λ) + (1 - ρ_{plastic}(λ))ρ_w(θ_s, θ_v, Δφ, λ) \quad (2)$$

where the geometrical and spectral dependencies are explicitly shown with $θ_s$, $θ_v$ and $Δφ$ the solar zenith, the viewing zenith and relative azimuth angles, respectively and $λ$ the wavelength. The reflectance due to all the plastic patches within a pixel ($ρ_{plastic}$), water surface reflectance accounting for specular reflection on the wave facets ($ρ_{spec}$), the above-water reflectance ($ρ_w$) accounting for absorption and scattering of water molecules and water content. We simplified $ρ_w$ to phytoplankton and their derivatives based on Morel model (Morel, 1988). Note that $ρ_{plastic}$ can be expressed in terms of the effective $R_{plastic}$ (Eq. 1), assumed Lambertian and taken from the measurement of wet material obtained at 100% pixel coverage, by the simple relation $ρ_{plastic} = f_{plastic} \times R_{plastic}$.

Radiative transfer computations were performed based on the documented plastic reflectances in order to quantify the atmospherically-induced modifications and the impact of plastic type and coverage on satellite measurements using the 6SV code (Kotchenova et al., 2006). Gaseous absorption was set to standard atmosphere for mid-latitude summer conditions (McClatchey et al., 1971). The aerosol content was selected as a continental aerosol model with an optical thickness of 0.2 at 550 nm. The plastic-free water surface was parameterised based on the Cox and Munk approach for a wind speed of 2 m/s (Cox and Munk, 1954). In our simulations we evaluated two observational geometric scenarios as examples to highlight the effects of viewing conditions for a passive satellite sensor. The first scenario involved a TOA signal without contributions from specular reflection or sea surface reflected sunglint, this allowed us to evaluate the satellite signal sensitivity to plastic concentrations and type where operational ocean colour algorithms are typically performed (Frouin et al., 2019). In the second scenario, TOA reflectances were derived for a viewing geometry that contained sunglint (Harmel and Chami, 2013; Harmel et al., 2018).

## 3. Results and discussion

### 3.1. Pixel coverage and reflectance

Spearman rank tests showed very strong positive monotonic
<figure><img src="image_7.png" /><figcaption>Fig. 4. Spectral reflectance ($R_{plastic}$) as a function of percentage pixel coverage by wet virgin and ocean-harvested samples at 850, 1215, 1732 nm.</figcaption></figure>

relationships between spectral reflectance (selected wavebands 850, 1215, 1732 nm) and percentage pixel coverage by most of the wet plastics (Fig. 4, Table 1). At pixel coverages lower than 20%, the hard fragments had enhanced reflectances (850 and 1215 nm) which was inconsistent with the expected positive correlation observed in the other specimens (Fig. 4).

We assumed non-linearity at reflectances close to zero or pixel coverages lower than 20%. Additionally, correlations at these wavebands were statistically insignificant exhibiting a very weak positive ($\rho = 0.25, -val = 0.60$) and very strong ($\rho = 0.83, p-val = 0.06$) relationship at 850 nm and 1732 nm respectively (Table 1). Similar to these hard fragments, statistics determined for HDPE samples were very weak (1215 nm, negative $\rho = -0.12, p-val = 0.79$) and (1732 nm, positive $\rho = 0.10, p-val = 0.84$). PP samples did have a strong positive (1732 nm, $\rho = 0.64, p-val = 0.10$) correlation that turned out to be statistically insignificant.

## 3.2. Dry and wet plastics

Dry plastics were observed to have higher reflectance compared to the wet samples (Fig. 5). All the wet samples were floating except for the LDPE which was slightly submerged. PS indicated the least loss of reflectance due to presence of water especially at 850 nm a likely result of its apparent white colour (Table 2). Only the bright reflective wet specimen of virgin PS and ocean-harvested foam had reflectances > 0.1 from the UV to SWIR spectrum (Fig. 7a). However, the transparent and slightly submerged LDPE had the lowest reflectances within the virgin plastics group (Fig. 5, Fig. 7a).

The general reduction of reflectance values in wet samples vs dry samples was expected and anticipated, as per previous works (Garaba and Dierssen, 2018), but the magnitude of the loss is of unclear origin and of larger nature than anticipated from theoretical work based in water extinction coefficients (Goddijn-Murphy and Dufaur, 2018). Thus,

a repetition of the experiments under wet conditions was required for discarding possible issues of that nature. However, our results just confirm what was previously reported and the question remains open. In addition, it is also not surprising that this behaviour varies according to the polymers, as it connects with the hydrophobic behaviour of the different plastic polymers under consideration.

## 3.3. Viewing angles

Spectral reflectance anisotropy of the plastics was further evaluated at diagnostic wavebands 850, 1215, 1732 nm of the virgin samples (Table 2). At different wavelengths for each polymer the spectral reflectance varied with the observation angle (Table 2 and Fig. 6). Our findings suggested a directional behaviour for the dry materials whilst the wet samples exhibited Lambertian properties. For example, dry HDPE at 850 nm had reflectance varying from 0.408 at $\theta = 0°$ to 0.506 at $30°$ nadir viewing angle but decreased at $\theta = 45°$ to 0.498. However, dry LDPE reflectance decreased from 0.106 ($\theta = 0°$) to 0.028 ($\theta = 45°$) at ~100% pixel coverage. Interestingly, the decrease in reflectance from dry to wet samples was less evident for LDPE than HDPE (Table 2). Absolute percentage differences (APDs) in LDPE actually increased (turned positive, suggesting increase in reflectance) with nadir observations angle from -64% at $\theta = 0°$ to 78% at $\theta = 45°$ at 850 nm this correlation was also noted at 1215 nm and 1732 nm. However, for HDPE, PP and PS this was not the case but rather minimal changes in APDs with the varying observations nadir angle as well as a general decrease in APDs (negative, suggesting loss of reflectance in water).

The same experiment was also completed to assess the effect of changing the viewing angle on the reflectance detectable from harvested floating ocean plastics. We realised a loss in spectral reflectance with increasing observation nadir angles (Fig. 6). At nadir $\theta = 0°$ the measured reflectances of all the samples was higher in comparison to those at nadir viewing angles $\theta > 0°$.
<caption>**Table 1**</caption>

Spearman rank test statistics relating reflectance to percentage pixel coverage at 0° nadir viewing angle, correlation coefficient (ρ), p-value (p-val) and number of variables (N). p-val < 0.05 indicated statistical significance.

<table><thead><tr><th rowspan="2">R<sub>plastic</sub></th><th colspan="3">850 nm</th><th colspan="3">1215 nm</th><th colspan="3">1732 nm</th></tr><tr><th>ρ</th><th>p-val</th><th>N</th><th>ρ</th><th>p-val</th><th>N</th><th>ρ</th><th>p-val</th><th>N</th></tr></thead><tbody><tr><td>HDPE</td><td>0.88</td><td>&lt; 0.05</td><td>8</td><td>-0.12</td><td>0.79</td><td>8</td><td>0.10</td><td>0.84</td><td>8</td></tr><tr><td>LDPE</td><td>1.00</td><td>&lt; 0.05</td><td>7</td><td>1.00</td><td>&lt; 0.05</td><td>7</td><td>1.00</td><td>&lt; 0.05</td><td>7</td></tr><tr><td>PP</td><td>0.93</td><td>&lt; 0.05</td><td>9</td><td>0.78</td><td>&lt; 0.05</td><td>9</td><td>0.64</td><td>0.10</td><td>8</td></tr><tr><td>PS</td><td>0.98</td><td>&lt; 0.05</td><td>8</td><td>0.93</td><td>&lt; 0.05</td><td>8</td><td>0.98</td><td>&lt; 0.05</td><td>8</td></tr><tr><td>Rope</td><td>0.96</td><td>&lt; 0.05</td><td>7</td><td>0.96</td><td>&lt; 0.05</td><td>7</td><td>0.93</td><td>&lt; 0.05</td><td>8</td></tr><tr><td>Sheets</td><td>0.90</td><td>&lt; 0.05</td><td>8</td><td>0.83</td><td>&lt; 0.05</td><td>8</td><td>0.82</td><td>&lt; 0.05</td><td>7</td></tr><tr><td>Foam</td><td>0.93</td><td>&lt; 0.05</td><td>9</td><td>0.98</td><td>&lt; 0.05</td><td>8</td><td>0.88</td><td>&lt; 0.05</td><td>8</td></tr><tr><td>Pellets</td><td>1.00</td><td>&lt; 0.05</td><td>6</td><td>1.00</td><td>&lt; 0.05</td><td>8</td><td>1.00</td><td>&lt; 0.05</td><td>6</td></tr><tr><td>Hard Fragments</td><td>0.25</td><td>0.60</td><td>7</td><td>0.93</td><td>&lt; 0.05</td><td>7</td><td>0.83</td><td>0.06</td><td>6</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Spectral reflectances (R<sub>plastic</sub>) at nadir viewing angle 0 = 0° with maximum pixel coverage for wet and dry high- and low-density polyethylene (HD, LD), polypropylene (PP), polystyrene (PS), rope (Ro), sheets (S), foam (F), pellets (P) and hard fragments (H).</figcaption></figure>

<caption>**Table 2**</caption>

Absolute percentage differences (APD) showing the loss of reflectance due to water of the virgin plastics at different nadir angles for ~100% pixel coverage.

<table><thead><tr><th rowspan="2"></th><th colspan="3">HDPE</th><th colspan="3">LDPE</th><th colspan="3">PP</th><th colspan="3">PS</th></tr><tr><th>Dry</th><th>Wet</th><th>APD</th><th>Dry</th><th>Wet</th><th>APD</th><th>Dry</th><th>Wet</th><th>APD</th><th>Dry</th><th>Wet</th><th>APD</th></tr></thead><tbody><tr><td>850 nm</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0°</td><td>0.408</td><td>0.237</td><td>-42</td><td>0.106</td><td>0.038</td><td>-64</td><td>0.368</td><td>0.232</td><td>-37</td><td>0.916</td><td>0.890</td><td>-3</td></tr><tr><td>15°</td><td>0.427</td><td>0.228</td><td>-47</td><td>0.080</td><td>0.049</td><td>-39</td><td>0.276</td><td>0.216</td><td>-22</td><td>0.883</td><td>0.871</td><td>-1</td></tr><tr><td>30°</td><td>0.506</td><td>0.225</td><td>-55</td><td>0.039</td><td>0.044</td><td>12</td><td>0.324</td><td>0.141</td><td>-57</td><td>0.861</td><td>0.870</td><td>1</td></tr><tr><td>45°</td><td>0.498</td><td>0.186</td><td>-63</td><td>0.028</td><td>0.051</td><td>78</td><td>0.256</td><td>0.107</td><td>-58</td><td>0.777</td><td>0.708</td><td>-9</td></tr><tr><td>1215 nm</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0°</td><td>0.154</td><td>0.036</td><td>-77</td><td>0.129</td><td>0.029</td><td>-77</td><td>0.233</td><td>0.020</td><td>-91</td><td>0.894</td><td>0.678</td><td>-24</td></tr><tr><td>15°</td><td>0.165</td><td>0.045</td><td>-73</td><td>0.081</td><td>0.039</td><td>-52</td><td>0.185</td><td>0.021</td><td>-88</td><td>0.861</td><td>0.651</td><td>-24</td></tr><tr><td>30°</td><td>0.199</td><td>0.023</td><td>-88</td><td>0.044</td><td>0.035</td><td>-21</td><td>0.203</td><td>0.012</td><td>-94</td><td>0.842</td><td>0.758</td><td>-10</td></tr><tr><td>45°</td><td>0.210</td><td>0.021</td><td>-90</td><td>0.034</td><td>0.044</td><td>27</td><td>0.166</td><td>0.010</td><td>-94</td><td>0.772</td><td>0.474</td><td>-39</td></tr><tr><td>1732 nm</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0°</td><td>0.039</td><td>0.015</td><td>-62</td><td>0.133</td><td>0.028</td><td>-79</td><td>0.070</td><td>0.013</td><td>-81</td><td>0.804</td><td>0.381</td><td>-53</td></tr><tr><td>15°</td><td>0.039</td><td>0.019</td><td>-52</td><td>0.073</td><td>0.035</td><td>-52</td><td>0.055</td><td>0.012</td><td>-79</td><td>0.769</td><td>0.345</td><td>-55</td></tr><tr><td>30°</td><td>0.043</td><td>0.014</td><td>-67</td><td>0.041</td><td>0.031</td><td>-25</td><td>0.061</td><td>0.008</td><td>-87</td><td>0.755</td><td>0.560</td><td>-26</td></tr><tr><td>45°</td><td>0.046</td><td>0.014</td><td>-70</td><td>0.033</td><td>0.041</td><td>21</td><td>0.053</td><td>0.008</td><td>-85</td><td>0.694</td><td>0.197</td><td>-72</td></tr></tbody></table>

### 3.4. Spectral shape and absorption features

Spectral shape variabilities were evaluated for the wet samples as the scope of our study was to assess remote sensing of floating ocean plastics from UV to SWIR (Fig. 7). Presence and location of absorption features in the spectra of virgin and ocean-harvested plastics was compared to previously reported infrared wavebands ~ 931, 1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm (Garaba and Dierssen, 2018). HDPE was observed to have matching absorption features at 1215, 1417 and 1732 nm. A common absorption feature in all the virgin plastics was centred around 990 nm (Fig. 7b). All ocean-harvested plastics had nearly matching absorption features at diagnostic wavebands 1215 and 1732 nm, slightly shifted absorption features were also found closer to

931 and 1417 nm (Fig. 7c). Spectral shape of the wet pellets gathered from this study matched well to the samples gathered from Kamilo Point in Hawaii (Garaba and Dierssen, 2019). Despite spectral shape similarity, the magnitude was different with the sample of this study suggesting lower reflectivity (Fig. 7d).

Descriptive statistics of the unbiased percentage difference (UPD) were 56 ± 17%, minimum UPD was 10% in the SWIR and a maximum of 82% in the VNIR spectrum. These pellets were objectively compared, a strong similarity was determined SCA = 5.6° and a statistically significant Spearman rank correlation coefficient ρ = 1, p-val < 0.05 between 850 and 2500 nm. PS was the only virgin plastic that had strong spectral shape similarities or SCA matches with the spectra ocean-harvested plastics, rope (SCA = 8.49°, ρ = 0.96, p-val < 0.05) and pellets (SCA =
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Viewing angle (0) effect on reflectance ($R_{plastic}$) at 850 nm, 1215 nm and 1732 nm for ~100% pixel coverage of wet sheets (S), foam (F), hard fragments (H), pellets (P) and rope (R).</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** (a) Measured reflectances ($R_{plastic}$) at maximum pixel coverage, (b-c) normalised to maximum reflectances of the wet virgin and ocean-harvested plastics and (d) measured reflectances of wet pellets from this study and Kamilo point in Hawaii (Garaba and Dierssen, 2019). Vertical grey lines indicate absorption features ~ 931, 1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm (Garaba and Dierssen, 2018).</figcaption></figure>

9.51°, $\rho$ = 0.94, *p*-val < 0.05). The sheets were also determined to have spectral shape resemblances with the pellets from this study (SCA = 9.20°, $\rho$ = 0.95, *p*-val < 0.05) and Kamilo Point in Hawaii (SCA=7.47°,

$\rho$ = 0.97, *p*-val < 0.05). An inter-comparison within the subsets of only the virgin plastics suggested moderate to very weak spectral shape similarities (10° < SCA < 42°) whereas the ocean-harvest subset
indicated better matches ranging from strong to very weak ($7^{\circ} < \text{SCA} < 29^{\circ}$).

### 3.5. Apparent colour effects on VNIR to SWIR reflectance

In the natural environment plastic litter is found in different shapes, sizes and visual colours. Here, a reasonable relationship between the magnitude of reflectance and the percentage pixel coverage by the plastics was derived at selected waveband (Fig. 4 and Table 1). Further analyses were conducted to advance knowledge about the effect of apparent colour on the spectral shape and magnitude from the VNIR to SWIR spectrum. As expected the multi-coloured dry HDPE pieces (orange, blue, white, purple, green, black) had lower reflectances compared to only white pieces (73.1, 79.6% pixel coverage) in the visible spectrum < 850 nm wavelength range. Only blue and white pieces of HDPE at full pixel coverage produced one major peak (blue, ~450 nm) and a minor peak (red, ~660 nm) in the visible spectrum (Fig. 8, inset). Reflectance of these blue and white pieces at ~100% coverage was also lower than that of the white pieces (73.1, 79.6% pixel coverage) between 520 and 670 nm. It is worth mentioning that the impact of apparent colours in plastics was not limited to spectral wavebands below 850 nm. A reduction of reflectance was also observed above 850 nm e.g. comparison between 79.6% and 99.31% coverage samples red line and black dots, respectively (Fig. 8). It is very possible that additives included in coloured plastics may be producing such additional absorption in this other segment of the spectra. However, the location of the absorption features in the SWIR spectrum seemed to be consistent despite the different apparent colours of the specimens.

A nearly flat but decreasing signal in the visible spectrum was noted for the white HDPE (pixel coverage < 99%). The multi-coloured pieces of HDPE had salient peaks matching the blue, green and red waveband ranges. Our measurements indicated that the apparent colour of objects at ~100% pixel coverage was influenced by the magnitude and shape of the reflectance from the UV to SWIR spectrum. Disregarding the multi-coloured pieces (100% and 99.31% pixel coverages), there was a reasonable positive linear trend at all wavelengths associating pixel coverage and spectral reflectance. There was a clear decrease in reflectance associated with the reduction of percentage pixel coverage between 79.6% and 15.77% (Fig. 8). It is important to note observations at 100% pixel coverage by only white HDPE pieces were not presented as there was no significant impact to our overall findings explained above.

### 3.6. Simulated TOA reflectances of plastics

TOA reflectances, without sunglint, were positively correlated with plastic coverage regardless of the plastic types (Fig. 9). Interestingly, these relationships were not linear contrarily to the linear correlation observed from the laboratory experiment where the effects of the atmosphere and sky reflection on the water surface were not considered (Fig. 4). It is also worth noting that two plastic types, LDPE and hard fragments, induced very small changes in the TOA reflectances as seen from the small increase both in absolute and relative values of the reflectance. For the other plastic types, the reflectance sensitivity to plastic coverage was highly dependent on the wavelength considered with the most sensitive spectral bands at the shorter wavelengths. However, the visible bands were the most impacted by the atmospheric effects (e.g., aerosol scattering) which makes a multispectral NIR-SWIR waveband approach more suitable for optical remote sensing of plastics from space.

For viewing geometry capturing sunglint, the TOA reflectances were negatively correlated with plastic coverage for most of the plastic types, namely, HDPE, LDPE, PP, Rope, Sheets, Pellets and Hard fragments (Fig. 10). The negative relationship can be explained by the fact that the presence of plastics on the water surface prevents the sun to be specularily reflected on the surface. In turn, the increase of plastic coverage diminishes the contribution of sunglint to the bulk reflectance measured

by the satellite sensor. This behaviour is more pronounced in the SWIR bands where the atmosphere, assuming little interaction with optically active gases, is more transparent meaning enhanced sunglint signal exiting to the TOA.

## 4. Discussion

### 4.1. Quality and representativeness of the documented reflectances

Statistical analyses indicated that, in general, the reflectance magnitude was proportional with percentage concentration of our plastic specimens. However, some exceptions were observed at lower pixel coverages. We derived relatively higher reflectances at these lower pixel coverages compared to those collected with higher concentrations of plastics, indicating possible detection limits for the specific materials. This was the case for the ocean-harvested sheets and hard fragments that had signs of bio-fouling and low reflectances (Fig. 1 and Fig. 7). It is important to consider evaluating the effects of bio-fouling on specimen as this will pose challenges in sensor design assuming bio-fouling masks and depletes the inherent signal of floating ocean plastics. We, therefore, believe there are prospects of detecting well aggregated bio-fouled plastics in accumulation zones, but challenges are to be expected when observing low concentrations. For the hard plastics, we found that reflectance was higher than expected at percentage coverages less than 20%. Therefore, further analyses are expected to investigate these anomalies at lower pixel coverages for various ocean-harvested plastics. We consider the wet virgin PS and ocean-harvested foam as our brightest targets because they had reflectances > 0.1 from UV to SWIR wavebands. These specimens generally presented high-floatability with a significant volume surfacing above the water likely contributing to the high reflectivity. They also tended to repel water at varying levels likely due to polymer type and state of biofouling/degradation that contributed to depleted signals due to water absorption.

All plastics displayed losses in reflectance when exposed to seawater consistent with prior studies (Garaba et al., 2018; Garaba and Dierssen, 2018). Presence of water on the samples caused a significant decrease in the reflectance of the object because water itself is a very strong light absorber (Kou et al., 1993). Dry LDPE at ~100% had higher reflectance in comparison to the wet LDPE. It can be explained by its transparent colour acting as a specular reflective surface but once underwater or submerged water absorbs most of the light hence lower reflectance. The refractive index of the plastic to air interface has been determined to be different from the plastic to water interface, due to geometry of the light source and detecting sensor as well as dry or wet state of samples. Contrary to the findings in our study, submerged wet polymers were previously reported to exhibit enhanced reflectance at viewing angles below 55° (Voss and Zhang, 2006), an aspect to be considered in future research. Detecting of virgin plastics composed of HDPE, PP, PS seemed achievable with the exception of wet and clear LDPE litter, it would require a sensor with a high signal to noise ratio especially if submerged. However, this type of marine litter is more water-repellent which means if they can float the reflectance detectable from any floating surface will not be highly affected by the strong absorption of light by water.

We also found differences in reflectance magnitude and shape at the investigated sensor viewing angles. These viewing angles were not statistically linked to the variations in reflectance magnitude and shape. In general, at different wavelengths the reflectance of the samples was heterogeneous albeit the correlation between reflectance and pixel coverage was relatively consistent. It is possible that the orientation of the samples with respects to the position of the sensor and light source has an effect on spectral reflectance measurements. In essence, the distance sensor-to-target might impose influence of the specific geometry and disposition of the samples in the pixel (in this study), introducing additional contributions to the signal that would be largely smoothed with sensors operating at much larger distances (satellite sensors) due to apparent size of the target features respect to the size of the sensor.
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** (a) Predetermined pixel coverages of 100, 80, 60, 40, 20, 10, 1, 0.1, 0.01% in red were recalculated using the RGB images and (b) Spectral reflectance ($R_{plastic}$) of dry HDPE at nadir viewing angle $θ = 0°$. Vertical grey lines indicate absorption features ~ 931, 1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm (Garaba and Dierssen, 2018).</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Top-of-atmosphere (TOA) reflectance with respect to the relative area of the pixel covered by plastics for several simulated satellite spectral bands (coloured lines), namely 550, 670, 870, 1020, 1600, 2200 nm. Computation were performed for a standard atmosphere with a continental aerosol model of optical thickness 0.2 at 550 nm and a viewing geometry avoiding sunglint ($\theta_s = 40^{\circ}$, $\theta_v = 5^{\circ}$ and $\Delta\phi = 90^{\circ}$) using 6SV radiative transfer code (Kotchenova et al., 2006).</figcaption></figure>

Additionally, as the viewing angle was varied from 0° to 45° the shape of the target region within the field-of-view changed from a circle to an ellipsoid, thus affecting effective bulk signal as pixel coverage changed. These aspects of object size, orientation and optical properties have been previously discussed but are not yet fully understood (Filella, 2015; Garaba et al., 2018; Goddijn-Murphy et al., 2018). Some of the challenges to fully understand the effects of anisotropy for the plastics rely on the respective roles of the roughness of the surfaces, inherent optical properties of the polymers and possible contribution of the surrounding optically active materials to the bulk spectral reflectance observed. Another possible source of uncertainty could have been the minor changes in the viewing angles as the frame was not entirely rigid. Here we mitigated effects of the surrounding by painting the surfaces black, applying a baseline correction and reduced uncertainties by conducting repetitive scans for each measurement.

Detection, identification and quantification algorithms widely used in remote sensing take advantage of diagnostic absorption features of optically active components such as vegetation, water, dissolved organic material or suspended sediments. Likewise, ocean-harvested plastics have been reported to have diagnostic wavebands ~ 931, 1045, 1215, 1417, 1537, 1732, 2046 and 2313 nm that were revealed in the spectra of our specimen here (Garaba and Dierssen, 2018). We believe there are prospects in using the wavebands at 1215 and 1732 nm in algorithm development considering they are less affected by optical properties of an intervening atmosphere. As for the virgin plastics, there is a need to pinpoint more generic or robust diagnostic wavebands. A common absorption feature was located at ~980 nm for the virgin plastics but is in the region affected by strong absorptions by atmospheric gases (Fig. 7b).

Even though we did not present these findings here, PP and PS were spectrally matched to an open-access spectral reference library indicating strong similarities (Garaba and Dierssen, 2017). LDPE had weak similarities due to the transparency effect as the samples in the open-access reference library were pellets of high opacity compared to the sampled material (Fig. 1). It echoes, therefore, the necessity of spectral reference libraries in advancing remote sensing datasets in scientific studies relevant to mapping plastic litter.

The apparent colour of the litter items was also shown to play a role in the spectral shape and magnitude from the UV to SWIR. In the visible spectrum the peaks observed corresponded to the visual colour of the objects but in the NIR to SWIR the absorption features remained salient (Fig. 8). Our study findings suggest that colour does matter when taking spectral reflectance measurements of plastics as algorithms that depend on the visible spectrum shapes might be prone to false positives. However, the colour of the samples did not affect the position of the absorption features in the NIR-SWIR spectrum. Clearly, the visible spectrum alone would not be sufficient in monitoring plastics without complementary descriptors such as imagery to determine shape and form. Thus, NIR-SWIR information offers the additional descriptors that can be leveraged to improve efforts of distinguishing plastics in marine litter. However, using purely NIR-SWIR wavebands for plastic litter monitoring is prone to raise false positives associated to the spectral shapes and absorption features of natural hydrocarbons such as floating seaweeds, phytoplankton blooms, oil slicks as well as issues with source of contamination in the image resulting from surface reflected glitter, sea foam, bubbles, white caps or breaking waves (Dierssen and Garaba, 2020). Therefore, any spectral remote sensing application for the
<figure><img src="image_13.png" /><figcaption>Fig. 10. Top-of-atmosphere (TOA) reflectance with respect to the relative area of the pixel covered by plastics for several simulated satellite spectral bands (coloured lines), namely 550, 670, 870, 1020, 1600, 2200 nm. Computation were performed for a standard atmosphere with a continental aerosol model of optical thickness 0.2 at 550 nm and a viewing geometry containing sunglint ($\theta_s = 10^\circ$, $\theta_v = 5^\circ$ and $\Delta\varphi = 90^\circ$) using 65V radiative transfer code (Kotchenova et al., 2006).</figcaption></figure>

detection of marine plastic litter shall operate in combination of visible and NIR-SWIR bands.

## 4.2. Implications for satellite remote sensing of plastics

As discussed above, combined information from radiometric measurements in the visible and NIR-SWIR might potentially disentangle ambiguities to help distinguish between plastic types and estimate their relative coverage within a satellite pixel. However, for satellite applications, the signal at the top-of-atmosphere (TOA) level is strongly impacted by the diffuse light of the atmosphere itself and extinction of light from BOA to TOA.

Based on this study, it can be argued that two main cases have to be established for plastic remote sensing: (i) glint-free case in which the plastic coverage induces non-linear increase of the TOA signal and (ii) pixel impacted by sunglint where plastics induce a strong signal which can be positively or negatively correlated. The second case might provide new insights on plastic coverage since the signal can be captured for all the plastic types analysed in this study. This sunglint approach has the potential to contribute to the general research effort to better observe and document the ocean surface content as it has been recently advocated (Neukermans et al., 2018).

# 5. Conclusions and outlook

The study provides spectral measurements and analyses that contribute to scientific evidence-based information key to advancing remote sensing of virgin and marine-harvested plastic litter. Strong

statistical correlations were determined between reflectance of plastics and the corresponding percentage pixel coverage. These correlations can be utilised to further identify and explore detection limits of plastics using diagnostic numerical solutions in radiative transfer models. Other approaches that derive indexes from band ratioing, depth or normalised difference algorithms also need to be considered in future evaluations at proposed wavebands specific and common to plastics. Our derived measurements also suggest that observations at low nadir angles ($\theta = 0^\circ$) had higher reflectances compared to those at higher nadir angles. We show that the apparent colour of objects does play a role in the spectral shape and magnitude of the bulk spectra. Weathered ocean-harvested plastics had nearly identical spectral shapes as well as absorption features, suggesting steps towards robust detection algorithms using diagnostic wavebands located in the SWIR, ~1215 and 1732 nm.

At the top-of-atmosphere level, the impact of plastic is modulated by the atmosphere itself, the sky and sun reflection on the plastic-free areas within the target considered. It has been shown based on the collected reflectances and full radiative transfer computation that the satellite signal is non-linearly correlated to plastic coverage. Two satellite remote sensing cases were established: (i) pixels free of sunlight reflection on the water surface for which the TOA signal had a positive correlation with plastic coverage and (ii) pixels impacted by sunglint where the simulated signal showed both positive and negative relationships for the different plastic types and wavebands. Remote sensing based on the sunglint approach appeared to be the most suitable in revealing new insights on plastic coverage since all the plastic types analysed here strongly impacted the derived TOA satellite signal.

More insights on the effect of water depth can be obtained by varying
and recording the depth of the water layer covering the samples. Using a fixed frame and adjusting the water layer (depth) could be a possibility. Additional fundamental inherent and apparent optical properties associated to the behaviour of light in the interfaces of plastic with air and water might also be required. Geometry analysis could be restricted to common satellite nadir viewing angles to further understand the effects of plastic piece orientation at different water depth is related to the spectral reflectance. Complementary works are urged to include detailed chemical characterisation of plastic litter as a key descriptor. Ground breaking findings to advance scientific evidence relevant to remote sensing strategies are expected from the ongoing and planned research efforts funded through the Discovery Element of the European Space Agency Basic Activities (ESA, 2020).

Remote sensing of ocean plastics in marine litter is gaining increasing interest from citizens, stakeholder and scientist. We believe it is crucial that research is dedicated towards exploring sensors capacities and methodologies to maximise the value and quality of data collected. Especially as a new generation of remote sensing technologies, platforms and algorithms are under development or have been launched. Success in harvesting information from remote sensing will vastly benefit from data that can be gathered by citizen smartphones, tablets, unmanned aerial systems, fixed observatories, High-Altitude Pseudo-Satellites, Cubesats and satellites. To this end, modern remote sensing technologies are thus anticipated to benefit research efforts in further establishing high quality open-access UV to SWIR hyperspectral reference libraries of optically active components found in the global oceans. Going forward, artificial intelligence algorithms can be extensively trained using high quality open-access hyperspectral reference libraries (virgin and ocean-harvested) to develop simple and sophisticated floating plastics detection algorithms.

## Disclaimer

The information in this manuscript reflects the views of the authors and does not reflect the official positions of the European Space Agency, Argans Limited, The Ocean Cleanup Foundation.

## Author contributions

SPG, MA, PC and LL designed the study. RdV assisted in sample preparation, experimental setup, data collection and processing. TH conducted the radiative transfer computations and prepared the related figures. SPG prepared the manuscript, figures and tables with the input of all authors. All authors reviewed and approved the manuscript text.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We are grateful to Julia Reisser for the groundwork on this study. We appreciate the assistance by Matthias Egger, Ilse Niessen and Anna Schwarz with data collection and sample preparations. Thank you Francesco Ferrari for designing the aluminium frame with a goniometer, supporting the sample preparation and discussions on the technical aspects of the experiment. Funding was provided by the General Studies Programme of the European Space Agency through the RESMALI project contract no. 4000121315/17/NL/PS, Discovery Element of the European Space Agency's Basic Activities contract no. 4000132037/20/NL/GLC and Deutsche Forschungsgemeinschaft grant no. 417276871.

# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jhazmat.2020.124290.

## References

* Andrady, A.L., 2011. Microplastics in the marine environment. Mar. Pollut. Bull. 62 (8), 1596-1605. https://doi.org/10.1016/j.marpolbul.2011.05.030.

* Cox, C., Munk, W., 1954. Statistics of the sea surface derived from sun glitter. J. Mar. Res. 13 (02-04), 198-227. http://images.peabody.yale.edu/publications/jmr/jmr13-02-04.pdf.

* Cózar, A., Echevarría, F., González-Gordillo, J.I., Irigoien, X., Úbeda, B., Hernández-León, S., Palma, Á.T., Navarro, S., García-de-Lomas, J., Ruiz, A., Fernández-de-Puelles, M.L., Duarte, C.M., 2014. Plastic debris in the open ocean. Proc. Natl. Acad. Sci. USA 111 (28), 10239-10244. https://doi.org/10.1073/pnas.1314705111.

* Dierssen, H.M., Garaba, S.P., 2020. Bright Oceans: Spectral Differentiation of Whitecaps, Sea Ice, Plastics, and Other Flotsam. In: Vlahos, P., Monahan, E.C. (Eds.), Recent Advances in the Study of Oceanic Whitecaps: Twist Wind and Waves: Cham. Springer International Publishing, pp. 197-208. https://link.springer.com/chapter/10.1007/978-3-030-36371-0_13.

* ESA, 2020. A step forward in detecting plastic marine litter from space. (https://www.esa.int/Enabling_Support/Preparing_for_the_Future/Discovery_and_Prepstation/A第一步Forward_in_detecting_plastic_marine_litter_from_space). (Last Accessed 28 August 2020).

* Filella, M., 2015. Questions of size and numbers in environmental research on microplastics: methodological and conceptual aspects. Environ. Chem. 12 (5), 527-538. https://doi.org/10.1071/EN15012.

* Forget, P., Quillon, S., Lahet, F., Broche, P., 1999. Inversion of reflectance spectra of nonchlorophyllous turbid coastal waters. Remote Sens. Environ. 68 (3), 264-272. https://doi.org/10.1016/S0034-4257(98)00117-5.

* Frouin, R.J., Franz, B.A., Ibrahim, A., Knobelspiesse, K., Ahmad, Z., Cairns, B., Chowdhary, J., Dierssen, H.M., Tan, J., Dubovik, O., Huang, X., Davis, A.B., Kalashnikova, O., Thompson, D.R., Remer, L.A., Boss, E., Coddington, O., Deschamps, P.-Y., Gao, B.-C., Gross, L., Hasekamp, O., Omar, A., Pelletier, B., Ramon, D., Steinmetz, F., Zhai, P.-W., 2019. Atmospheric correction of satellite ocean-color imagery during the PACE era. Front. Earth Sci. 7 (145) https://doi.org/10.3389/feart.2019.00145.

* Garaba, S.P., Aitken, J., Slat, B., Dierssen, H.M., Lebreton, L., Zielinski, O., Reisser, J., 2018. Sensing ocean plastics with an airborne hyperspectral shortwave infrared imager. Environ. Sci. Technol. 52 (20), 11699-11707. https://doi.org/10.1021/acs.est.8b02855.

* Garaba, S.P., Dierssen, H.M., 2017. Spectral reference library of 11 types of virgin plastic pellets common in marine plastic debris, Data set. Available-on-line [http://ecosis.org] from theEcological Spectral Information System (EcoSIS), doi:10.21232/C27H34.

* Garaba, S.P., 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 Sens. Environ. 205, 224-235. https://doi.org/10.1016/j.rse.2017.11.023.

* Garaba, S.P., Dierssen, H.M., 2019. Spectral reflectance of dry and wetmarine-harvested microplastics from Kamilo Point, Pacific Ocean, Data set. Available on-line [http://ecosis.org] from the Ecological Spectral Information System (EcoSIS), doi:10.21232/r7gg-yv83.

* Garaba, S.P., Dierssen, H.M., 2020. Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics. Earth Syst. Sci. Data 12 (1), 77-86. https://doi.org/10.5194/essd-12-77-2020.

* Goddijn-Murphy, L., 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., 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 (Suppl. C), 255-262. https://doi.org/10.1016/j.marpolbul.2017.11.011.

* Harmel, T., Chami, M., 2013. Estimation of the sunglint radiance field from optical satellite imagery over open ocean: multidirectional approach and polarization aspects. J. Geophys. Res. Oceans 118 (1), 76-90. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JC008221.

* Harmel, T., Chami, M., Tormos, T., Reynaud, N., Danis, P.-A., 2018. Sunglint correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands. Remote Sens. Environ. 204, 308-321. https://doi.org/10.1016/j.rse.2017.10.022.

* Koeppke, P., 1984. Effective reflectance of oceanic whitecaps. Appl. Opt. 23 (11), 1816-1824. https://doi.org/10.1364/AO.23.001816.

* Kotchenova, S.Y., Vermote, E.F., Matarrese, R., Klemm, J.F.J., 2006. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance. Appl. Opt. 45 (26), 6762-6774. https://doi.org/10.1364/AO.45.006762.

* Kou, L., Labrie, D., Chylek, P., 1993. Refractive indices of water and ice in the 0.65- to 2.5-µm spectral range. Appl. Opt. 32 (19), 3531-3540. https://doi.org/10.1364/AO.32.003531.

* 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., Reisser, J., 2018. Evidence that the Great Pacific Garbage
Patch is rapidly accumulating plastic. Sci. Rep. 8 (1), 4666. https://doi.org/
10.1038/s41598-018-22939-w

Mace, T.H., 2012. At-sea detection of marine debris: overview of technologies, processes, issues, and options. Mar. Pollut. Bull. 65 (1–3), 23–27. https://doi.org/10.1016/j.
marpolbul.2011.08.042.

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., Vethaak, A.D., 2019. Measuring marine plastic debris from
space: initial assessment of observation requirements. Remote Sens. 11 (20), 2443.
https://doi.org/10.3390/rs11202443.

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., Wilcox, C., 2019. Toward the integrated marine debris observing system. Front. Mar. Sci. 6 (447) https://doi.org/10.3389/
fmars.2019.00447.

Maximenko, N., Hafner, J., Kamachi, M., MacFadyen, A., 2018. Numerical simulations of debris drift from the Great Japan Tsunami of 2011 and their verification with observational reports. Mar. Pollut. Bull. 132, 5–25. https://doi.org/10.1016/j.
marpolbul.2018.03.056.

McClatchey, R.A., Fenn, R.W., Selby, J.E.A., Volz, F.E., Garing, J.S., 1971. Optical Properties of the Atmosphere (Revised). In: Environmental Research Papers. Air Force Cambridge Research Laboratories, Bedford, Massachusetts, USA, AFCRL-71-0279. https://apps.dtic.mil/dtic/tr/fulltext/u2/726116.pdf.

Morel, A., 1988. Optical modeling of the upper ocean in relation to its biogenous matter content (case I waters). J. Geophys. Res. Oceans 93 (C9), 10749–10768. https://doi.
org/10.1029/JC093iC09p10749.

Neukermans, G., Harmel, T., Galí, M., Rudorff, N., Chowdhary, J., Dubovik, O.,
Hostetler, C., Hu, Y., Jamet, C., Knobelspiesse, K., Lehahn, Y., Litvinov, P., Sayer, A.
M., Ward, B., Boss, E., Koren, I., Miller, L.A., 2018. Harnessing remote sensing to
address critical science questions on ocean-atmosphere interactions. Elem. Sci.
Anthr. 6 (71–46), 71. https://doi.org/10.1525/elementa.331.

Ryabinin, V., Barbière, J., Haugan, P., Kullenberg, G., Smith, N., McLean, C., Troisi, A., Fischer, A., Aricò, S., Aarup, T., Pissierssens, P., Visbeck, M., Enevoldsen, H.O., Rigaud, J., 2019. The UN decade of ocean science for sustainable development. Front. Mar. Sci. 6 (470) https://doi.org/10.3389/fmars.2019.00470.

van Sebille, E., Aliani, S., Law, K.L., Maximenko, N., Alsina, J.M., Bagaev, A., Bergmann, M., Chapron, B., Chubarenko, I., Cózar, A., Delandmeter, P., Egger, M., Fox-Kemper, B., Garaba, S.P., Goddijn-Murphy, L., Hardesty, B.D., Hoffman, M.J., Isobe, A., Jongedijk, C.E., Kaandorp, M.L.A., Khatmullina, L., Koelmans, A.A., Kukulka, T., Laufkötter, C., Lebreton, L., Lobelle, D., Maes, C., Martinez-Vicente, V., Morales Maqueda, M.A., Poulain-Zarcos, M., Rodríguez, E., Ryan, P.G., Shanks, A.L., Shim, W.J., Suaria, G., Thiel, M., van den Bremer, T.S., Wichmann, D., 2020. The physical oceanography of the transport of floating marine debris. Environ. Res. Lett. 15 (2), 023003 https://doi.org/10.1088/1748-9326/ab6d7d.

Smail, E.A., DiGiacomo, P.M., Seeyave, S., Djavidnia, S., Celliers, L., Le Traon, P.-Y., Gault, J., Escobar-Briones, E., Plag, H.-P., Pequignet, C., Bajona, L., Zhang, L., Pearlman, J., Steven, A., Hodge, J., Racault, M.-F., Storlazzi, C., Skirving, W., Hoeke, R., Marra, J., van Dongeren, A., Muller-Karger, F., Cripe, D., Takaki, D., 2019. An introduction to the 'Oceans and Society: Blue Planet' initiative. J. Oper. Oceanogr. 12 (Suppl. 2), S1–S11. https://doi.org/10.1080/
1755876X.2019.1634959.

Suaria, G., Avio, C.G., Mineo, A., Lattin, G.L., Magaldi, M.G., Belmonte, G., Moore, C.J., Regoli, F., Aliani, S., 2016. The Mediterranean Plastic Soup: synthetic polymers in Mediterranean surface waters. Sci. Rep. 6 (1), 37551. https://doi.org/10.1038/
srep37551.

Voss, K.J., Zhang, H., 2006. Bidirectional reflectance of dry and submerged Labsphere Spectralon plaque. Appl. Opt. 45 (30), 7924–7927. https://doi.org/10.1364/
AO.45.007924.

Wan, K.X., Vidavsky, I., Gross, M.L., 2002. Comparing similar spectra: from similarity index to spectral contrast angle. J. Am. Soc. Mass Spectrom. 13 (1), 85–88. https://doi.org/10.1016/S1044-0305(01)00327-0.