Proof of concept for a model of light reflectance of plastics floating on natural waters

An inversion model of microplastics abundance based on satellite remote sensing: a case study in the Bohai Sea

Nowadays, microplastics (MPs) as emerging contaminants have posed great risks to marine ecosystems and human health. However, non-continuous field sampling data makes it difficult to meet the needs of scientific research and pollution control of marine MPs. Consequently, the development of rapid monitoring techniques for marine MPs to achieve efficient acquisition of data is increasingly essential. Remote sensing technology provides a convenient and effective tool for monitoring and mapping marine MPs pollution. Therefore, we established an inversion model based on multiple regression by combining the remote sensing data and the measured data to predict the MPs pollution status in the Bohai Sea. The feature variables of a model are crucial to the prediction, and we proposed three methods of variable selection, namely successive projections algorithm (SPA), band combination method, and remote sensing index method. By comparing accuracy evaluation metrics, an approach based on SPA was selected to analyze the abundance and spatio-temporal distribution of MPs in the Bohai Sea in 2022. The determination coefficient of the SPA model is 0.75, and the root mean square error is 0.38 items/m3. The error of the model is within an acceptable range. It was found that the MPs abundance on the sea surface of the Bohai Sea varied significantly in different seasons and regions. This study indicates that satellite remote sensing technology has great potential in monitoring marine MPs.

Marine plastic pollution detection and identification by using remote sensing-meta analysis

The persistent plastic litter, originating from different sources and transported from rivers to oceans, has posed serious biological, ecological, and chemical effects on the marine ecosystem, and is considered a global issue. In the past decade, many studies have identified, monitored, and tracked marine plastic debris in coastal and open ocean areas using remote sensing technologies. Compared to traditional surveying methods, high-resolution (spatial and temporal) multispectral or hyperspectral remote sensing data have been substantially used to monitor floating marine macro litter (FMML). In this systematic review, we present an overview of remote sensing data and techniques for detecting FMML, as well as their challenges and opportunities. We reviewed the studies based on different sensors and platforms, spatial and spectral resolution, ground sampling data, plastic detection methods, and accuracy obtained in detecting marine litter. In addition, this study elaborates the usefulness of high-resolution remote sensing data in Visible (VIS), Near-infrared (NIR), and Short-Wave InfraRed (SWIR) range, along with spectral signatures of plastic, in-situ samples, and spectral indices for automatic detection of FMML. Moreover, the Thermal Infrared (TIR), Synthetic aperture radar (SAR), and Light Detection and Ranging (LiDAR) data were introduced and these were demonstrated that could be used as a supplement dataset for the identification and quantification of FMML.

Sources, sinks and transformations of plastics in our oceans: Review, management strategies and modelling

Currently, 60-80 % of litter is plastic, and almost 10 % ends up in the ocean directly or indirectly. Plastics often suffer from photooxidation producing microplastics and these microplastics derived from the breakdown of larger plastics are called secondary microplastics. These compounds simply cannot be extracted from the oceans, and once mixed, they enter the food chain and may have toxic effects. This work reviews the current existing information on the topic in the scientific literature. Then, the current plastic management strategies in the marine environment are analysed, with the objective of identifying possible needs and improvements from a sustainable point of view, and to define new approaches. Simultaneously, a material flows analysis in different media of the marine environment is carried out using system dynamics. A preliminary model of plastics mobilization into the ocean to other media of the marine environment (like sediments and biota) is developed and validated with the existing data from the previous steps of the work. This work expands the current knowledge on the plastics management, their transformations and accumulation in the marine environment and the harmful effects on it. Likewise, preliminary dynamic model of mobilization of plastics in the ocean is implemented, run, and validated. The developed model can be used to predict trends in the distribution of the plastics in the ocean with time. In addition, the most important reservoirs of plastics in the ocean can be observed. Although plastics undergo transformations in the marine environment, it is not a means of disposal since most of them are non-biodegradable. Most plastics accumulate on the seabed. The proportion of microplastics found in sediments is higher than that of macroplastics.

Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques

Sentinel-2 (S2) images have been used in several projects to detect large accumulations of marine litter and plastic targets. Their limited spatial resolution though hinders the detection of relatively small floating accumulations of marine debris. Thus, this study aims at overcoming this limit through the exploration of fusion with very high-resolution WorldView-2/3 (WV-2/3) images. Various state-of-the-art approaches (component substitution, spectral unmixing, deep learning) were applied on data collected in synchronized acquisitions of plastic targets of various sizes and materials in seawater. The fused images were evaluated for spectral and spatial distortions, as well as their ability to spectrally discriminate plastics from water. Several WV-2/3 band combinations were investigated and five litter indexes were applied. Results showed that: a) the VNIR combination is the optimal one, b) the smallest observable plastic target is 0.6 × 0.6 m² and c) SWIR bands are important for marine litter detection.

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review

Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018 techniques based on remote sensing and artificial intelligence are advancing. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review summarizes state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.

Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter

In recent years, the remote sensing of marine plastic litter has been rapidly evolving and the technology is most advanced in the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) wavelengths. It has become clear that sensing using VIS-SWIR bands, based on the surface reflectance of sunlight, would benefit from complementary measurements using different technologies. Thermal infrared (TIR) sensing shows potential as a novel method for monitoring macro plastic litter floating on the water surface, as the physics behind surface-leaving TIR is different. We assessed a thermal radiance model for floating plastic litter using a small UAV-grade FLIR Vue Pro R 640 thermal camera by flying it over controlled floating plastic litter targets during the day and night and in different seasons. Experiments in the laboratory supported the field measurements. We investigated the effects of environmental conditions, such as temperatures, light intensity, the presence of clouds, and biofouling. TIR sensing could complement observations from VIS, NIR, and SWIR in several valuable ways. For example, TIR sensing could be used for monitoring during the night, to detect plastics invisible to VIS-SWIR, to discriminate whitecaps from marine litter, and to detect litter pollution over clear, shallow waters. In this study, we have shown the previously unconfirmed potential of using TIR sensing for monitoring floating plastic litter.

Detection of River Plastic Using UAV Sensor Data and Deep Learning

Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

Top-of-atmosphere hyper and multispectral signatures of submerged plastic litter with changing water clarity and depth

The exploitation of satellite remote sensing is expected to be a critical asset in monitoring floating and submerged plastic litter in all aquatic environments. However, robust retrieval algorithms still havel to be developed based on a full understanding of light interaction with plastic litter and the other optically active constituents of the atmosphere-water system. To this end, we performed laboratory-based hyperspectral reflectance measurements of submerged macroplastics under varying water clarity conditions (clear - 0 mg/L, moderate - 75 mg/L, very turbid - 321.3 mg/L) and submersion depths. This comprehensive optical dataset was used (i) to relate the plastic-related signal to submersion depth and turbidity parameters, and (ii) to investigate the top-of-atmosphere signal through full radiative transfer calculations. Simulated TOA radiation was used to explore the nominal pixel and spectral requirements based on WorldView-3, Sentinel-2, and Sentinel-3 missions with very high to moderate geo-spatial resolutions. Results showed that plastics remained detectable when submerged in the top ∼1 m of the water column regardless of water clarity conditions. At TOA, uncertainties attached to atmospheric correction were shown to be reasonable and acceptable for plastic detection purposes in the infrared part of the spectrum (> 700 nm). The impact of aerosols on the TOA signal was found to be complex as (i) over large plastic patches. The aerosols produced little impact on satellite observations mostly due to adjacency effects and (ii) optical signature from isolated/small extent plastic patches was critically altered suggesting the atmospheric transmittance should be accurately corrected for in plastic detection algorithms. The sensitivity analyses also revealed that the narrow band widths of Sentinel-3 did not improve detection performance compared to the WorldView-3 coarser band widths. It is proposed that high spatial resolution wavebands such as the pan-chromatic could be advantageously explored for submerged plastic monitoring applications.

Development of automated marine floating plastic detection system using Sentinel-2 imagery and machine learning models

The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%-90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.

Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis

Marine plastic debris (MPD) is a globally relevant environmental challenge, with an estimated 8 million tons of synthetic debris entering the marine environment each year. Plastic has been found in all parts of the marine environment, including the surface layers of the ocean, within the water column, in coastal waters, on the benthic layer and on beaches. While research on detecting MPD using remote sensing is increasing, most of it focuses on detecting floating debris in open waters, rather than detecting MPD on beaches. However, beaches present challenges that are unique from other parts of the marine environment. In order to better understand the spectral properties of beached MPD, we present the SWIR reflectance of weathered MPD and virgin plastics over a sandy substrate. We conducted spectral feature analysis on the different plastic groups to better understand the impact that polymers have on our ability to detect synthetic debris at sub-pixel surface covers that occur on beaches. Our results show that the minimum surface cover required to detect MPD on a sandy surface varies between 2–8% for different polymer types. Furthermore, plastic composition affects the magnitude of spectral absorption. This suggests that variation in both surface cover and polymer type will inform the efficacy of beach litter detection methods.

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems.

Towards the Spectral Mapping of Plastic Debris on Beaches

Floating and washed ashore marine plastic debris (MPD) is a growing environmental challenge. It has become evident that secluded locations including the Arctic, Antarctic, and remote islands are being impacted by plastic pollution generated thousands of kilometers away. Optical remote sensing of MPD is an emerging field that can aid in monitoring remote environments where in-person observation and data collection is not always feasible. Here we evaluate MPD spectral features in the visible to shortwave infrared regions for detecting varying quantities of MPD that have accumulated on beaches using a spectroradiometer. Measurements were taken from a range of in situ MPD accumulations ranging from 0.08% to 7.94% surface coverage. Our results suggest that spectral absorption features at 1215 nm and 1732 nm are useful for detecting varying abundance levels of MPD in a complex natural environment, however other absorption features at 931 nm, 1045 nm and 2046 nm could not detect in situ MPD. The reflectance of some in situ MPD accumulations was statistically different from samples that only contained organic debris and sand between 1.56% and 7.94% surface cover; however other samples with similar surface cover did not have reflectance that was statistically different from samples containing no MPD. Despite MPD being detectable against a background of sand and organic beach debris, a clear relationship between the surface cover of MPD and the strength of key absorption features could not be established. Additional research is needed to advance our understanding of the factors, such as type of MPD assemblage, that contribute to the bulk reflectance of MPD contaminated landscapes.

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

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.

Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment

While at least 8 million tons of plastic litter are ending up in our oceans every year and research on marine litter detection is increasing, the spectral properties of wet as well as submerged plastics in natural marine environments are still largely unknown. Scientific evidence-based knowledge about these spectral characteristics has relevance especially to the research and development of future remote sensing technologies for plastic litter detection. In an effort to bridge this gap, we present one of the first studies about the hyperspectral reflectances of virgin and naturally weathered plastics submerged in water at varying suspended sediment concentrations and depth. We also conducted further analyses on the different polymer types such as Polyethylene terephthalate (PET), Polypropylene (PP), Polyester (PEST) and Low-density polyethylene (PE-LD) to better understand the effect of water absorption on their spectral reflectance. Results show the importance of using spectral wavebands in both the visible and shortwave infrared (SWIR) spectrum for litter detection, especially when plastics are wet or slightly submerged which is often the case in natural aquatic environments. Finally, we demonstrate in an example how to use the open access data set driven from this research as a reference for the development of marine litter detection algorithms.

Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

The threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new technologies and the automation of analytical processes. In the current study, we developed algorithms capable of detecting and quantifying FMML in aerial images, and a web-oriented application that allows users to identify FMML within images of the sea surface. The proposed algorithm is based on a deep learning approach that uses convolutional neural networks (CNNs) capable of learning from unstructured or unlabelled data. The CNN-based deep learning model was trained and tested using 3723 aerial images (50% containing FMML, 50% without FMML) taken by drones and aircraft over the waters of the NW Mediterranean Sea. The accuracies of image classification (performed using all the images for training and testing the model) and cross-validation (performed using 90% of images for training and 10% for testing) were 0.85 and 0.81, respectively. The Shiny package of R was then used to develop a user-friendly application to identify and quantify FMML within the aerial images. The implementation of this, and similar algorithms, allows streamlining substantially the detection and quantification of FMML, providing support to the monitoring and assessment of this environmental threat. However, the automated monitoring of FMML in the open sea still represents a technological challenge, and further research is needed to improve the accuracy of current algorithms.

Nano-plastics and their analytical characterisation and fate in the marine environment: From source to sea

Polymer contamination is a major pollutant in all waterways and a significant concern of the 21st Century, gaining extensive research, media, and public attention. The polymer pollution problem is so vast; plastics are now observed in some of the Earth's most remote regions such as the Mariana trench. These polymers enter the waterways, migrate, breakdown; albeit slowly, and then interact with the environment and the surrounding biodiversity. It is these biodiversity and ecosystem interactions that are causing the most nervousness, where health researchers have demonstrated that plastics have entered the human food chain, also showing that plastics are damaging organisms, animals, and plants. Many researchers have focused on reviewing the macro and micro-forms of these polymer contaminants, demonstrating a lack of scientific data and also a lack of investigation regarding nano-sized polymers. It is these nano-polymers that have the greatest potential to cause the most harm to our oceans, waterways, and wildlife. This review has been especially ruthless in discussing nano-sized polymers, their ability to interact with organisms, and the potential for these nano-polymers to cause environmental damage in the marine environment. This review details the breakdown of macro-, micro-, and nano-polymer contamination, examining the sources, the interactions, and the fates of all of these polymer sizes in the environment. The main focus of this review is to perform a comprehensive examination of the literature of the interaction of nanoplastics with organisms, soils, and waters; followed by the discussion of toxicological issues. A significant focus of the review is also on current analytical characterisation techniques for nanoplastics, which will enable researchers to develop protocols for nanopolymer analysis and enhance understanding of nanoplastics in the marine environment.

Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

Finding Plastic Patches in Coastal Waters using Optical Satellite Data

Satellites collecting optical data offer a unique perspective from which to observe the problem of plastic litter in the marine environment, but few studies have successfully demonstrated their use for this purpose. For the first time, we show that patches of floating macroplastics are detectable in optical data acquired by the European Space Agency (ESA) Sentinel-2 satellites and, furthermore, are distinguishable from naturally occurring materials such as seaweed. We present case studies from four countries where suspected macroplastics were detected in Sentinel-2 Earth Observation data. Patches of materials on the ocean surface were highlighted using a novel Floating Debris Index (FDI) developed for the Sentinel-2 Multi-Spectral Instrument (MSI). In all cases, floating aggregations were detectable on sub-pixel scales, and appeared to be composed of a mix of seaweed, sea foam, and macroplastics. Building first steps toward a future monitoring system, we leveraged spectral shape to identify macroplastics, and a Naïve Bayes algorithm to classify mixed materials. Suspected plastics were successfully classified as plastics with an accuracy of 86%.

Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements

Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.

On Thermal Infrared Remote Sensing of Plastic Pollution in Natural Waters

Plastic pollution in the world’s natural waters is of growing concern and currently receiving significant attention. However, remote sensing of marine plastic litter is still in the developmental stage. Most progress has been made in spectral remote sensing using visible to short-wave infrared wavelengths where optical physics applies. Thermal infrared (TIR) sensing could potentially monitor plastic water pollution but has not been studied in detail. We applied radiative transfer theory to predict TIR sensitivity to changes in the surface fraction of water covered by plastic litter and found that the temperature difference between the water surface and the surroundings controls the TIR signal. Hence, we mapped this difference for various months and times of the day using global SST (sea surface temperature) and t2m (temperature at 2 m height) hourly estimates from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The maps show how SST-t2m difference varied, altering the anticipated effectivity of TIR floating plastic litter remote sensing. We selected several locations of interest to predict the effectivity of TIR sensing of the plastic surface fraction. TIR remote sensing has promising potential and is expected to be more effective in areas with a high air–sea temperature difference.