Proof of concept for a new sensor to monitor marine litter from space

Domain: earth_science
Published: 2024-06-14
Editorial outcome: major_revision

Abstract
Worldwide, governments are implementing strategies to combat marine litter. However, their effectiveness is largely unknown because we lack tools to systematically monitor marine litter over broad spatio-temporal scales. Metre-sized aggregations of floating debris generated by sea-surface convergence lines have been reported as a reliable target for detection from satellites. Yet, the usefulness of such ephemeral, scattered aggregations as proxy for sustained, large-scale monitoring of marine litter remains an open question for a dedicated Earth-Observation mission. Here, we track this proxy over a series of 300,000 satellite images of the entire Mediterranean Sea. The proxy is mainly related to recent inputs from land-based litter sources. Despite the limitations of in-orbit technology, satellite detections are sufficient to map hot-spots and capture trends, providing an unprecedented source-to-sink view of the marine litter phenomenon. Torrential rains largely control marine litter inputs, while coastal boundary currents and wind-driven surface sweep arise as key drivers for its distribution over the ocean. Satellite-based monitoring proves to be a real game changer for marine litter research and management. Furthermore, the development of an ad-hoc sensor can lower the minimum detectable concentration by one order of magnitude, ensuring operational monitoring, at least for seasonal-to-interannual variability in the mesoscale.

Introduction
Marine litter (ML) mirrors the failures of our civilization to manage waste1,2. In a few decades, ML has grown into a global environmental threat, recognised as an urgent priority on international sustainability agendas3,4. Governments and businesses around the world are rolling out a plethora of measures to combat ML. However, field surveys are proving insufficient to track ML trends on a global scale, and the effectiveness of the action plans against ML remains uncertain5.

ML is any human-made item that ends up in marine environments. The diversity is huge, but about 80% of these items are made of plastic (95% in the ocean surface waters)6, making this material the main concern for managers4 and the focus of monitoring5. The first global maps of plastic debris became available for the ocean surface a decade ago7,8. Continued efforts to extend data coverage have produced important results9, but field measurements remain sparse and unexplored areas still cover most of the ocean. In this framework, satellite-based observations are one of the best hopes for large-scale monitoring of ML10. Yet, the fraction of ocean surface physically covered by plastic in the most polluted regions is estimated to be below 0.02%, from 200 to 0.1 parts per million11,12. The weakness of the radiative signal reflected by the main component of ML imposes strong constraints for its direct monitoring from space, at least following the standard approaches used for other ocean constituents such as phytoplankton or suspended sediments.

An alternative opportunity to monitor ML from space lies in the use of proxies, like the metre-sized aggregations of floating debris, the so-called surface slicks, streaks, filaments or litter windrows (LWs)13–15. The action of wind, tides, river plumes and ocean density currents often generate surface convergence zones in the sub-mesoscale domain, which may eventually form dense clusters of floating material. LWs typically take filamentous shapes a few metres wide and from tens to thousands of metres long13,16, showing litter densities up to 10,000-fold higher than in their surroundings17. LWs can theoretically be used to flag episodes of severe surface pollution13. However, the use of these sparse and ephemeral targets for ML monitoring requires testing whether their abundance is sufficient to capture spatio-temporal patterns at any scale, and knowing the true meaning of this proxy.

The objective of the present study is twofold. First, it aims to define an optimal Earth-Observation (EO) mission concept for the global-scale monitoring of ML, referred to as the EO4ML mission. Second, the study conducts a proof of concept to test the feasibility and usefulness of EO4ML by implementing it with available technology on a real-world case study. On the basis of the optimal EO concept, we derived a detector for use with the multispectral acquisitions of the in-orbit Copernicus Sentinel-2 sensor. This suboptimal detector was applied on a continuous basis throughout the Mediterranean Sea, selected as our region of interest for its notable socio-ecological value and high ML pollution18.

Results
Optimal concept and proof of concept
Plastic materials have an unequivocal human origin and are predominant among floating ML6. Therefore, the definition of the optimal mission concept for surface ML monitoring used plastic debris as the core target (Fig. 1). In the first step, reflectance spectra were obtained for varying concentrations and plastic polymers, then incorporating then other materials relevant to the radiative signal of floating litter19,20 (Supplementary Information [SI] Sections S1.2.1 and S3.1.1, Fig. S3). This spectral library was fed into an atmospheric radiative transfer model in order to determine spectral radiances at the top of the atmosphere. The isolation of spectral bands for the sensor and their width was achieved by optimising the remote sensing performance (as signal-to-noise ratio) with changing fractions of plastic polymers over a range of observation conditions (SI Sections S1.2.2 and S3.1.2, Figs. S13 to S21). A set of additional bands was configured to specifically address the quantification of other abundant floating materials (i.e. algae, driftwood and seafoam) and to support atmospheric correction and cloud detection (Table S3). This exercise allowed us to define functionalities for a total of 23 candidate spectral bands (Table 1, Figs. S22 to S24).

The minimum threshold requirements for an EO4ML mission would involve a Super-Spectral Instrument with at least four bands in the near-infrared and two bands in the short-wavelength infrared, a configuration resulting from the significance of infrared radiation in the spectral signature of plastic. The proposed sensor could provide true detection from the natural sunlight reflectance of an ocean plot with a minimum surface plastic coverage of 1% (SI Section S3.2.1). However, this threshold is unlikely to be achieved extensively over the ocean surface11,12, so even an optimal sensor would be obliged to focus on detecting LWs, and use a pixel size at least in the typical range of LW widths (~10 m) to increase the probability of exceeding the 1% threshold. By setting the spatial resolution at 10 m, a two-satellite constellation would achieve global coverage with a revisit time of a few days, a time resolution benchmark for many of the applications identified by the community10.

The in-orbit Multi-Spectral Instrument of the EU Copernicus Sentinel-2 mission (S2-MSI) presents some compatibility with the technical requirements of an optimal EO4ML mission concept, in particular spectral bands within the infrared region with medium-high spatial resolution (10–20 m). These features provided us with the opportunity to carry out a proof of concept (PoC) to test the applicability of LWs as a proxy for ML monitoring (Fig. 2). By adapting an infrared spectral index for this sub-optimal sensor (SI Section 2.1.7), the minimum plastic surface coverage required to achieve detection was estimated to be close to 20% (SI Section 2.4, Fig. S9). However, other drifting materials commonly included in the LWs such as weeds, driftwood or metastable seafoam, may also contribute to the S2-MSI detections21. Thus, the most reasonable option for the detector in the PoC was a probabilistic dichotomous (presence/absence) classification of image pixels with or without plastic-like spectral profiles. In addition, the detector applied a strict contextual filtering (SI Section 2.1.8) to deal with the spectral noise in the S2-MSI measurements21. Thus, only those clusters of pixels with positive detection, resembling filament-shaped patches longer than 70 m, were taken into account. While spectral detection was targeted on plastic, PoC detections must be considered here as ML aggregations containing varying fractions of both anthropogenic and natural debris due to the known limitations of the S2-MSI21. Eventually, with dedicated spectral bands (Table 1), it will be possible to disentangle the contributions of the main litter components (e.g. plastic, weeds, driftwood, metastable seafoam) and estimate plastic concentrations per pixel. Importantly, an optimal sensor would also allow a proper account of small scattered litter patches (<70 m), which should result in a significant increase in the number of detections and the capability for defining spatio-temporal patterns.

Marine litter windrows
A total of 14,374 LWs were detected and supervised in the 75-month monitoring of the Mediterranean Sea from July 2015 to September 2021 (Fig. 3). To date, only a few hundred LWs have been characterised, mainly because of the difficulties encountering these short-lived structures at sea13. The 14,374 LWs covered 94.5 km2 of sea surface, an area equivalent to 7,500 football fields. The size distribution of the filaments detected from space agreed with reported measurements from ships13,16, predominantly about 1 km long (Fig. S28). Nevertheless, remote detections also included unprecedented LW lengths. Twenty-seven filaments (0.2% of the total) were longer than 10 km, up to 23 km long.

There is a wide array of hydrodynamic processes able to generate the submesoscale convergence structures necessary to form LWs13. Several authors pointed to surface fronts associated with internal waves as the predominant mechanism of LW formation22,23. The orientation of LWs in the Mediterranean, mainly parallel to shoreline and isobaths (Fig. 2b), fits with this proposal23. Notwithstanding, other convergence processes like estuarine fronts, open-ocean density fronts and Langmuir circulation were also apparent among our detections.

Litter windrow density
The raw output of the detector used in the present PoC is a categorical classification of pixels, with either the presence or absence of dense litter cover, mapping LWs at a spatial resolution of 10 m. Quantitative assessment of ML pollution on a continuous scale was done through the fraction of sea surface area covered by LWs, referred to as litter-windrow density (LWD, in ppm; e.g. m2 per km2). Importantly, LWD was calculated solely over the sea surface under environmental conditions deemed suitable for litter aggregation, i.e. for winds lower than 5 m s−1 (see “Methods”). By reducing spatio-temporal bias due to different wind conditions and, consequently, varying conditions for LW formation, this wind-based constraint was intended to improve the comparability of LW detections in space and time, an issue hitherto unaccounted for.

On average, LWD for the whole Mediterranean was 0.2 ppm. For reference, the fraction of the Mediterranean Sea surface covered by plastic debris measured with surface-trawling nets is about 1.0 ppm (i.e. m2 of plastic per km2 of sea surface, accounting for both scattered and clustered plastics)12. LWD decreased exponentially with distance to shore, suggesting a strong control of the release of litter from land on the distribution of LWs (Fig. 4). Nevertheless, this decreasing trend showed consistent irregularities, mainly a litter build-up in the 5 to 30 km distance to land, which persisted seasonally (Fig. S30). Furthermore, the LWD peak at around 10 km matched the maximal LW lengths. This litter build-up seems to be the result of a bottleneck in the seaward propagation of litter, most likely caused by the strong currents commonly flanking the shoreline, the so-called boundary currents. In the Mediterranean Sea, they form a counterclockwise circulation belt that reaches, on average, its maximum strength at 50 km from land (yellow line in Fig. 4). Field samplings have already reported the blocking effect on the offshore transport of litter in the Northern Current, the regional boundary current in the north-western Mediterranean24. In our satellite images, this effect was particularly evident when coastal jets cut through highly littered waters (e.g. off the coast of France), or in the low permeability of the steady gyres placed in sub-basins such as the Alboran and Adriatic Seas (Figs. 3 and 5).

Control of spatial patterns
The basin-scale mapping of LWD showed hotspots scattered predominantly in nearshore waters, particularly in the western and central Mediterranean (Fig. 5). The most intense hotspots were located in the south-western Alboran Sea (LWD = 9.1 ppm), Algerian waters (12.9 ppm), Gulf of Gabes (15.8 ppm), off Calabria (south-west Italy, 9.0 ppm), and, most notably, at the northern end of the Adriatic Sea (55.8 ppm; a region-specific analysis is presented in SI Section S4). In contrast, the south-eastern basin had the most extensive clean waters, which is consistent with the basin-scale surveys conducted for both microplastics12 and human-made mega-litter25.

Most models of land-to-ocean plastic inputs (PI) predict an opposite spatial distribution of sources if compared to our LWD map or to previously reported patterns of floating plastic12,25, which increase from the south-eastern to the north-western Mediterranean. Current PI models rely on estimates of mismanaged plastic waste (MPW) on land, assuming that the efficiency of MPW transfer from land to ocean (η) is invariant in space1 and time26. Therefore, these models predict that the north-western shores, with high economic status and presumably effective waste management, emit little plastic1,2. However, field observations provide growing evidence of a strong control of environmental drivers on litter emissions, namely precipitation and surface runoff27–29. Introducing environmental control over η, PI predictions were significantly correlated with nearshore LWD (Fig. 6a, Fig. S31). Lack of precipitation in the south-eastern watersheds (<100 mm y−1) accounts for the relatively low LWD in their adjacent waters. On the other extreme, watersheds on the north-western shores receive up to 30 times more precipitation, especially on the Alpine watersheds in Italy, connected to the major LWD hotspot in the Mediterranean (Fig. 5).

The spatial correlation found between PI and LWD implies that much of the litter remains near its land-based source. In this regard, boundary currents were presented in the previous section as important transport barriers. However, these barriers are also susceptible to both along-shore and cross-shore transport, leading to important deviations in the PI-LWD correlation (Fig. 6). First, the counterclockwise circulation of the boundary current system may entail relevant advection alongside the coastline. Major alongshore pathways appeared to run from the North Adriatic hotspot through a corridor along the Italian eastern coastline, from north Tunisia to the Gulf of Gabes, and through the Asia Minor Current to the Rhodes Basin (Figs. 3 and 6b). Secondly, floating litter may eventually cross-boundary currents, especially in events of massive PI and strong seaward winds24. ML release into open waters was particularly evident in the Sea of Sardinia, receiving inputs from France and Algeria (Fig. 5). The most striking seaward surge of LWs occurred in spring 2018 (Fig. 7b), coincident with record-breaking rainstorms and flooding along the northwestern Mediterranean coast (southern France and northeastern Spain)28,30. The meteorological conditions during this event, as well as field surveys during similar events24, point to the northwesterly wind flow (Mistral) as the driver of this massive transport of litter, intruding more than 300 km offshore. Mistral is the strongest wind system in the Mediterranean31, and it is possibly involved in the above-normal LWD observed in the western waters of Corsica and Sardinia and off the French Riviera (Fig. 6b). Anyhow, despite the occasional relevance of along-shore and cross-shore transport, the PI-LWD linkage remained broadly significant at basin scale. With due caveats, this spatial correlation suggests a primary relationship of LWs with nearby (<150 km) litter sources and, particularly, with plastic emissions (Fig. S31). Plastic litter is highly persistent, in fact, the majority of all floating items in the Mediterranean Sea are massively conveyed to the sea, to inland trapping sites from where it is difficult to re-mobilize, or even buried27–29,35.

Control of temporal patterns
The dynamics of ML hotspots were characterised by pronounced pulses, often associated with record rains and floods over land (Fig. 7). Short and strong LWD peaks every 2 to 3 years generally accounted for more than half of all LW detections, suggesting that LW formation is particularly reactive to extreme rainstorm events.

Rainstorm systems typically show a diameter of a few hundred kilometres and travel thousands of kilometres across the Mediterranean during their lifetime (2–8 days)34. On a basin-wide scale, the pulsating LWD dynamics induced by these cyclones were dampened by the multiplicity and asynchrony of local pulses, but basin-wide LWD also trailed precipitation and runoff trends (Fig. 8a). The rainiest periods were followed by increases in LWD, mainly in autumn 2016, spring and autumn 2018, and spring 2021. The main uncoupling between precipitation and LWD was found in the second half of 2019 (high precipitation and decreasing LWD). In the previous year, both precipitation and LWD reached the highest values in the time series, twice as much as in other years, which agrees with the annual litter inputs measured in rivers28,29. The intense flushing of watersheds during the wet 2018 could have depleted the MPW available for runoff transport and thus explain the weak response to precipitation in 2019. When rivers overflowed in 2018, field surveys reported how MPW was massively conveyed to the sea, to inland trapping sites from where it is difficult to re-mobilize, or even buried27–29,35.

At a seasonal scale, rainfall in the Mediterranean typically extends from early autumn to spring36. In contrast, the seasonal pattern of LWD was bimodal, with peaks in autumn and spring separated by a steep winter minimum (Fig. 8b). The relative importance of the two seasonal LWD peaks changed from west to east, in accordance with the timing of the cyclonic systems hitting each sub-basin. Spring rainstorms impacted mainly the western Mediterranean30, triggering the highest LWD peaks. The Atlantic Ocean supplies many of the cyclones entering the Mediterranean, resulting in a longer and more intense rainy season in the west36. Likewise, cyclogenic activity within the Mediterranean takes place mainly over the western and central sub-basins34. In our observation period, the autumn formation of storms and tropical-like cyclones (e.g. Zorbas medicane37) controlled the seasonal LWD pattern in the central Mediterranean. In the south-eastern Mediterranean, the lack of rainfall and reduced runoff were apparently the main reasons behind the low LWD, with only small coastal hotspots (e.g. Nile delta, off Lebanon). The weak connection to land-based sources was already mentioned to explain the extraordinary paucity of microplastics found in the south-eastern Mediterranean12.

Unlike the spring and autumn peaks, the winter decline in LWD was highly consistent year to year over the entire basin, being captured by ship-based litter monitoring38,39. Seasonal wind speed maximum is also highly steady throughout the entire Mediterranean Sea40, matching the LWD minimum from December to January (grey dotted line in Fig. 8b). Given that LWD is calculated only over the sea surface suitable for LW formation (winds < 5 m s−1), wind effect should mainly be reflected in LWD through the advection of floating litter or its removal from the sea surface by beaching. In the North Adriatic, floating litter was systematically cleared every year during the wind peaks in December and January (Figs. S26 and S32). We hypothesised an increased wind-driven dragging of surface litter towards coastal waters and onto the shores in winter. This onshore transport of floating litter was supported by a clear seasonal shift in the spatial distribution of LWs over the Mediterranean. In summer, LWD presented a smooth profile with distance to land, while offshore pollution decreased and litter retreated to coastal waters in winter (Fig. 4b). LWD decreases in the Mediterranean Sea during the high wind season, but winter LWD exceeds the summer LWD in the water strip closest to shore (<50 km from land). Seasonal patterns of beach litter are difficult to unravel because they are strongly influenced by the waste input from summer beach tourism. However, considering only data from non-tourist beaches in the Mediterranean, beach litter loads corroborated the hypothesised increase in litter stranding towards winter41,42.

A new scenario for research and management
The PoC demonstrated the potential of an EO mission using sub-mesoscale surface aggregation structures as a proxy for monitoring ML pollution. Despite the sensor limitations used in the PoC and the ephemeral nature of the LWs, the number of detections gathered over a 6-year period proved sufficient to delimit mesoscale accumulation zones and capture interannual and seasonal patterns. Maps and trends of LWD showed high consistency with available field data12,25,29,38,39,41,42, as well as with both environmental (P, wind, MKE) and anthropogenic (population, MPW) drivers, composing an unprecedented basin-scale picture of the source-to-sink ML dynamics. Land-based mismanaged waste and torrential rains controlled the inputs of ML, while coastal boundary currents and wind-driven surface sweep were the key drivers for its distribution over the sea surface.

LW detections were primarily related to the location and magnitude of land-based ML inputs during the previous weeks. This feature renders the LW proxy particularly helpful for surveillance and management purposes. Using real case scenarios, Supplementary Information illustrates some applications, such as the identification of pollution hotspots, shipping-related sources of litter, long-term evaluation of action plans or better targeting of clean-up efforts (SI Section 4, Fig. S33). The readiness level of the present monitoring tool allows for its implementation in other ocean regions; however, there is still much room for improvement. S2-MSI surveys in other ocean regions could advance the mission concept, analysis methods and data interpretation. Current LWD estimates account for wind conditions since LW formation requires not only high ML load but also favourable sea state13. Nevertheless, further refinement of LWD estimates should ideally account, along with disruptive forces (e.g. wind mixing), for major formation forces (e.g. tidal forcing).

The next steps should also advance on the ground truthing (Fig. 1). The current matching of satellite detections and field observations is limited to fake targets (artificial LWs)15,20 and reports of dense LW sightings14 (Figs. S9 and S11). Filling this gap requires targeted and coordinated research on the LW phenomenon and the combination of several approaches. Fake targets are an effective approach, which might be further extended to different LW compositions and environmental conditions. Matching detections of natural LWs and field measurements is a more challenging task due to the difficulty of finding LWs from ship surveys13. We can now take advantage of satellites to address areas and periods of common LW formation (Fig. 5). Complementary observation platforms with larger coverage areas than ships (e.g. long-range drones, aircrafts) could also significantly increase LW findings25. Additional inputs for calibration and validation can be gained from the ocean-wide spreading of animal-borne cameras43, particularly from species using windrows as feeding spots. Citizen science could also contribute with opportunistic observations whereas from leisure vessels, fishing, or commercial ships. Finally, another pivotal approach stems from the overlapping of S2-MSI detections with observations from medium-to-high-resolution hyperspectral satellites44.

The sustained monitoring of LWs from space can be a game changer for ML research and management and could also be of interest beyond the ML problem. LWs trace currents, retention zones and repelling structures from submeso- to macro-scale, opening new prospects for understanding the multiscale interplay of ocean transport. A relevant ecological implication of LWs relates to their role as hubs of marine life on the ocean surface, generating aggregations of small organisms and attracting large predators (e.g. fish, seabirds)13,23. The main limitation of the in-orbit S2-MSI for ML monitoring is its sensitivity, since it requires long and dense LW structures to provide reliable detections. An optimal sensor could reduce the detection threshold by one order of magnitude, as well as supporting the unmixing of LW components. Accounting for litter components would diversify the application possibilities beyond plastic-specific monitoring (e.g., metastable seafoam in eutrophication). Side applications derived from targeting metre-sized items on the ocean surface might cover areas such as cargo loss, oil spills, navigational safety, or search and rescue at sea. Overall, the concept of an EO mission dedicated to ML monitoring proves to be not only plausible but also highly promising.

Methods
Optimal mission concept
The aim of the mission conceptualization was to establish the minimum base requirements for an EO mission founded on a spectral analyser instrument with dedicated bands for marine plastic litter (EO4ML mission). To do so, we carried out a combined analysis of user needs, monitoring scenarios as well as ML physical properties potentially useful for space-based sensors. Both observational capabilities and technical requirements were contrasted with existing and near-future technologies. The data for this exercise were generated in laboratory, field, and through modelling tests. Ultimately, the trade-off analysis provided the key theoretical specifications for the EO4ML mission, particularly on the identification of candidate spectral bands and signal-to-noise ratios. The full process for defining the optimal mission concept is described, step by step, in Supplementary Sections S1 and S3.

Proof of concept
The PoC was based on the finding of limited compatibility between the spectral bands identified for an optimal mission and the infrared bands of the Multi-Spectral Instrument of the Sentinel-2 mission (S2-MSI). The EU Copernicus Sentinel-2 mission takes images over a grid with 100×100 km2 tiles and a spatial resolution up to 10×10 m2. The revisiting time for the Mediterranean is 2-3 days, with two satellites operating simultaneously (since 7 March 2017). For the present PoC, a total of 288,166 images (150 TB of data) were processed in a High-Performance Computing facility, corresponding to 411 tiles covering the entire Mediterranean Sea and for the period from 4 July 2015 to 21 September 2021 (75 months).

Building on the knowledge gained in the optimal mission concept, we developed a specific spectral index to classify S2-MSI pixels with and without plastic-like signatures (SI Section 2.1.7). After the spectral classification of individual pixels, the detector performs filtering to select only those aggregations of pixels with positive detections resembling filament-shaped patches over 70 m long, with the aim of minimising the number of false positives (SI Section 2.1.8). In a later step, the image clippings of each candidate filament automatically detected by the detector were supervised by a team of 6 researchers (SI Section 2.3.3). Six different contributors to false positives were recognized in the supervision, i.e. aeroplane exhausts, cloud edges, wave glint, ship trails, human-made structures and image stitching (Figs. S34 to S39). LW clippings are archived in an open repository for future development of automatic classification methods (https://doi.org/10.5281/zenodo.11045944). Supplementary Section S2 provides a detailed description of the PoC detector (image pre-processing, spectral indices, and filament contextual identification), detector tests, and supervision of the results.

Litter windrow density
The raw output of the PoC detector (i.e. filaments with dense ML coverage) was expressed as a quantitative variable using the area of LWs relative to the observed sea surface suitable for litter aggregation. Mathematically, this litter-windrow density (LWD, in ppm) was calculated as:

LWD = a_lw / a_o * 10^6 (1)

where a_lw is the surface area of LWs, and a_o is the total sea surface area observed by the sensor (not covered by clouds) and deemed as suitable for LW formation. In this regard, we removed from a_o the sea surface area associated with wind speeds higher than 5 m·s−1. Neutral wind speeds at 10 m above the sea surface (u_10N) were acquired from the ERA5 database40. We found LWs with u_10N up to 11.2 m·s−1, however, the probability of detection dropped sharply above 5 m·s−1 (Fig. S27). More than 95% of all detections were below this threshold, likely due to vertical and horizontal mixing by wind and wave action. Interestingly, the threshold of 5 m·s−1 was also defined as that above which vertical mixing of floating plastic debris becomes significant45.

For our study scale, we selected a 0.25-degree latitude-longitude grid as the standard binning for LWD computation. Note that the average LWD for a large region (e.g. mesoscale ML hotspot) is independent of the grid selected, but finer grids (smaller cells) result in larger statistical dispersion of LWD values across grid cells, with lower minima and higher maxima.

Apart from the average statistic, we used the maximal grid-averaged (LWD_mga) to characterise mesoscale ML hotspots. LWD_mga corresponds to the spatial maximum of the annual averages in the 0.25-degree latitude-longitude grid over the referenced region. The annual average for each grid cell was estimated by averaging the means for the 12 months of the year, using data over the entire study period. Cells containing a sea area of less than 100 km2 were discarded for LWD_mga, as excessively small sea areas may be unrepresentative and reach extreme maxima (>1,000 ppm). Unlike the spatial average, LWD_mga shows the advantage of being less dependent on where the boundaries of the hotspots are placed.

Spatio-temporal drivers
Given the paucity of field data with high spatio-temporal resolution, the consistency of the LWD observations was in large part tested on its relationships with environmental drivers and the production of mismanaged plastic waste (MPW). This was done separately for the spatial and temporal variability, using the time-averaged map and the basin-wide timeseries, respectively. The time-averaged map of LWD has the advantage of a weak dependence on the temporal response to drivers such as rainfall, which could include time lags or gaps in response to the eventual depletion of land-based MPW. In the case of the LWD time series for the entire Mediterranean basin, we took advantage of the fact that they are independent of hydrodynamics and surface transport of litter.

Spatial analyses
The time-averaged map of LWD in the Mediterranean Sea was contrasted with available models of plastic emissions from land. For this purpose, the first step was the exploration of the performance of the main existing models of land-based production of MPW (MPWP), as well as possible upgrades (Table S32). MPWP (in t y−1) for watershed i is commonly estimated as:

MPWP_i = p_i * g_c * f_c * in_c + l (2)

where p_i is the population in watershed i (inhab, restricted to the population living within 200 km from the coastline); g_c is the per-capita waste generation rate for country c where i is located (t inhab−1 y−1); f_c is the fraction of plastic in the waste stream in c; l is a common (country-independent) fraction of littered waste; and in_c is the fraction of inadequately managed waste for c, which is based on the economic status of c. A total of 1,562 watersheds were delimited within the Mediterranean basin from the HydroSHEDS database46. p_i was estimated from high resolution (0.5 arc minutes, ~1 km) gridded population density data47; while l, g_c, f_c and in_c were obtained from the model by Jambeck et al.1 (J_WB), and from the low, mid and high-range models by Lebreton and Andrady48 (LA_L, LA_M, LA_H, respectively). We also tested a version of Jambeck’s model based on data from Waste Atlas49 (J_WA model) instead of that of the World Bank50. Moreover, an additional model was only scaled with the population size, following the suggestion by Weiss and coworkers51 (W model). For this last approach, a general per-capita MPW generation was applied (i.e. 13.5 kg inhab−1 y−1), corresponding to the global average of the mid-range model by Lebreton and Andrady48. Note that the election of this per-capita rate conditions the magnitude of MPWP but not its relative spatial variability. In all, six different models were explored for MPWP (Table S32).

Land-to-ocean transfer rate of MPW (η, in y−1) is likely key for understanding marine plastic pollution, but little is known about its magnitude and spatio-temporal variability. Here, we used precipitation and surface runoff as drivers of η, as proposed by a growing body of field studies27–29. Precipitation and runoff at a spatial resolution of 6 arc minutes (~10 km) were obtained from the ERA5-land dataset52. Both variables (expressed in mm y−1) were converted into η units (y−1) by scaling to a Mediterranean basin-wide average of 0.25 y−1, corresponding to the global mid-range η suggested by Jambeck and coworkers1. Again, this linear transformation was just intended to maintain the dimensional coherence, having no effect on its relative variability.

Plastic input into the ocean (PI, t y−1) in watershed i was calculated as the product of MPW_i (t) and η_i (y−1):

PI_i = MPW_i * η_i (3)

For simplicity, MPW_i available for runoff transport was assumed to be that generated in the current year (i.e. MPWP_i Eq. 2), in accordance with previous PI models1,2.

The analysis of the correspondence between potential explanatory variables (MPWP, η and PI) and the spatial LWD pattern was addressed on the nearshore waters in order to reduce the role of hydrodynamics and transport of floating litter. Since LWD off a watershed outlet also depends on the plastic emissions from neighbouring watersheds, a range of circular buffers of different sizes was used to calculate LWD and independent variables (MPWP, η, PI). Centred at each single watershed outlet, we used circular buffers ranging from 10 to 200 km radius. Thus, LWD associated with a watershed outlet was calculated over the sea surface within the buffer zone, and PI was the sum of all the emissions from the watershed outlets within the buffer.

Finally, we performed a Pearson product-moment correlation test between nearshore LWD and predictive variables. A logarithmic transformation (log(x+1)) was applied due to the wide range and skewness of the data analysed. The raw correlation (without log transformation) delivered higher Pearson coefficients (up to r=0.9), however, this was due to an over-fitting of the high values, which minimizes the residual sum of squares but disregards data in the low range.

Of the six MPWP models explored (Table S32), the one based on population alone (W model) presented the highest spatial correlation with LWD (Fig. S31a). High range model by Lebreton and Andrady48 (LA_H) also showed high Pearson coefficients (r), as it set the highest fraction of littered waste (l=10%) and consequently gave the greatest weight to population in the MPWP estimate. This result suggests that factors other than economic status (e.g. consumer habits, beach tourism41) could also be playing a significant role in MPWP. Interestingly, the drivers to estimate η (i.e. precipitation and runoff) were better predictors of LWD than MPWP. Lastly, as expected, PI achieved the highest correlations with LWD, in particular PI models based on a highly population-dependent MPWP (W·P and W·R models). The Pearson coefficients for the W·R model increased with buffer size, reaching the highest at 150 km radius (Fig. S31b). In order to use a model with both high r and high spatial resolution, we selected the W·R model with 150 km buffers for the LWD spatial analysis (Fig. 6).

Temporal analyses
Exploring the correlation between LWD and PI estimates over time involves further complexity due to the limited knowledge of the time variability of MPW available for runoff transport1. Accumulation or even depletion of land-based MPW could happen in particularly dry or wet years, respectively; and these changes likely also occur at seasonal or shorter scales due to the wide variability of both precipitation and surface runoff. Thus, for example, the first rains after summer could have a strong response in LWD, while later rains could have no effect on litter emissions.

Based on the strong control of precipitation and surface runoff on the spatial variability of LWD (Fig. S31), we opted to explore the monthly variability of basin-wide LWD from direct correlation with precipitation and runoff (as major drivers of PI). To address the complexity of the LWD response at seasonal or shorter scales, we extracted interannual trends of precipitation, runoff and LWD by subtracting seasonal components53 and applying 5-month moving average filters. LWD changes may be also decoupled from precipitation and runoff due to delays in river discharges and LW formation or to the accumulation of floating litter after consecutive months of rainfall. Estimates of residence time of litter on the sea surface usually range from weeks to months54. Hence, the relationship between the basin-wide trends of LWD and predictive variables (precipitation and runoff) was tested over a range of lag times from 0 to 6 months.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Comments to ms by Cozar et al. (see also the annotated manuscript):\nGeneral evaluafion: This manuscript presents interesfing and potenfially valuable results based on a new\napproach of detecfing windrows from space, and confirming via spectral profiling that these windrows\ncontain floafing anthropogenic lifter, in parficular plasfics. The general findings appear reasonable,\nnamely more windrows in coastal regions, more lifter windrows (LW) close to coastal sources and more\nabundant after rainfalls. Also the comparison of LWs with Plasfic Inputs (PIs) appears suitable and\ngenerates very useful results (it basically is the Proof of Concept). I congratulate the authors for the idea\nand also for the immense work with the image processing and the data analysis.\nWhile the key results (figures 1-7) are impressive and relafively easy to follow, the structure of the\nmanuscript is confusing and especially the concepts presented in the text require revision and focusing.\nThe authors present a number of constructs (e.g. the “PoC detecfion processor”) that are confusing in\ntheir logical concepfion and presentafion. For example, they introduce the “Proof of Concept”, which\ncertainly is a very useful and valid approach (and the PoC itself is not the problem). In the present\ncontext, the PoC could mean that they propose a stepwise approach that passes through the remote\ndetecfion of windrows, then the spectral profiling of plasfic lifter (which idenfifies among all the\ndetected windrows those that indeed contain large quanfifies of plasfic lifter and results in the LWs),\nand\nvia quanfificafion of size (length and area) of these LW, they then determine the LWD (in ppm, which\nseems to be prefty much “m2 per km2”). In a next step they then correlate these LWDs with esfimated\nPlasfic lifter Inputs (PI), which is based on input source (=rivers?), runoff, and populafion density. They\nfind a reasonably good correlafion between LWD and PI, which, of course, is not perfect, but they\nidenfify the areas where important deviafions from the overall correlafion are found, and they provide\nreasonable arguments to explain these deviafions (more local processes that cause these deviafions).\nAll this together (correlafion between LWD & PI; with explanafion of major deviafions) then could be\nconsidered as a first “Proof of Concept” for this new approach. This seems to be the underlying\n“concept” (sic) of this study, and the main results follow this line of argumentafion and tesfing of the\nPoC.\nHowever, the authors then create substanfial confusion by introducing the “PoC detecfion processor”,\nwhich is a very odd construct. The PoC is here apparently converted to a specific “detector” that uses\ndifferent spectral approaches and compufing analysis steps to idenfify individual LWs. This (detecfion of\nWs and idenfificafion of LW) is certainly an important part of this approach, but the linguisfic construct\nof a “PoC detecfion processor” is very confusing and I strongly recommend to find a different construct\nfor this, because the PoC is the umbrella concept that embeds the enfire study: if the authors can show\nthat their approach provides decent results that are useful to idenfify areas with high amounts of PL\n(accumulated in LW), then this is a reasonable Proof of Concept for their approach. In your analyfical\napproach you certainly can have a detecfion processor, but I strongly recommend to give this a different\nidenfify than “PoC detecfion processor”.\nAnother issue is the fact that the current approach only idenfifies LWs even though it seems that the\nauthors might also able to idenfify Windrows (Ws). I have to admit that I did not read the enfire 137\npages of the electronic supplement, but I glanced through it and looked at different secfions in order to\nbefter understand the overall approach. On P. 29 of the supplement, the authors say “To ensure our\nfocus on LWs, we combine the spectral index with a contextual filter based on the geometry of such\nstructures (secfion 2.1.8), and a further filtering was applied during post-processing (secfions 2.2.2 and\n2.3.3) to ensure that only LWs with high ML density were preserved for the PoC analysis.” In the context\nof this study, it seems very important to understand where windrows are and which of these windrows\nreally contain ML, and maybe even how much ML. As discussed in some of my comments in the\nannotated manuscript, I suspect that the rainfalls flush a lot of natural vegetafion into the sea, and that\ninifially the Ws have a low proporfion of ML, but that over fime the proporfion of ML increases (because\nthe plasfic lifter persists while natural vegetafion disintegrates and sinks). I understand that this quesfion\nis not the goal of this study, but the proporfion of Ws that have ML and therefore are classified as LWs\ndoes seem highly relevant and should be explored and presented if at all possible.\nIn general, the text somefimes is very difficult to follow, which is also due to the fact that the authors use\na lot of abbreviafions, not all of which are intuifive. I would advise to use fewer abbreviafions and only\nfocus on the most important and most frequently used ones. Similarly, concentrafing the main text on\nthe key results (figures 1 – 7) will substanfially improve the manuscript.\n1) Title: ok\n2) Abstract: ok\n3) Introducfion: ok, mofivates the present study well.\n5) Methods: it would be good to add a conceptual figure in the manuscript that illustrates the key steps\nin this new approach; the figures in the supplement are very extensive and detailed, and a synthefic\noverview figure could be useful (while maintaining the detailed figures in the supplement).\n6) Results & Discussion: the inifial results are very interesfing and nicely illustrate the stepwise approach\nand findings, but starfing with figure 8, it seems that not all that info is necessary. In fact, starfing with\nthe subchapter “The case of the North Adriafic”, it seems that informafion becomes too detailed and\nsome aspects are repefifive. It seems that the Adriafic subchapter, if it were to be maintained, should be\nmoved to the electronic supplement. The subchapters about the opfimal mission concept and then the\nscenario for research and management go beyond the principal goals of this study and should actually\nbe drasfically condensed and converted into to a very short and concise conclusion chapter. The current\nlength of this results & discussion secfion substanfially dilutes the overall impact of this new approach.\n7) References: I have not checked whether all references are cited in the text and viceversa, but I feel\nthat the authors could possibly draw a bit more on the Sargassum studies, which have followed a\nsomewhat similar approach, for example (and several other papers): Dierssen, H.M., Chlus, A. and\nRussell, B., 2015. Hyperspectral discriminafion of floafing mats of seagrass wrack and the macroalgae\nSargassum in coastal waters of Greater Florida Bay using airborne remote sensing. Remote Sensing of\nEnvironment, 167, pp.247-258.\nIn summary, I think that this could become an important contribufion, but it will require major revision\nand careful focusing of the text. This will likely mean a lot of addifional work but I think that it is mainly\ncareful crafting of the text, and so it should be doable. I encourage the authors to take on this task,\nbecause it will ensure the future impact of their paper and approach.\nIn case of any quesfions about my comments, I invite the authors to contact me directly.\nSincerely, Marfin Thiel", "reviewer_2": "The paper reports on a very wide study aimed at defining specificafions for an earth observafion satellite\nmission with the purpose of detecfing plasfic lifter at sea.\nAfter mofivafing the use of a proxy (lifter windrows) for detecfions, rather than aiming at the direct\ndetecfion of scaftered plasfic items, the authors study a very large dataset covering the whole\nMediterranean Sea for a long period of fime, taken from Senfinel data, to make a convincing feasibility\nstudy for the mission.\nBased on simulafion of the reflectance properfies of water, and of natural and arfificial materials\n(specifically, the plasfic polymers that are most represented in marine lifter), at reasonable\nconcentrafions for real-world scenarios, an evaluafion of the SNR obtained at satellite vs. the wavelength\nof received radiafion (from UV to SWIR) is obtained. The most relevant bands according to the SNR for\nplasfics, as well as for confounding materials, are chosen, discussing in this way the specificafion of a\n\"super-spectral\" sensor system for the mission envisioned.\nThis work is very relevant to a task that has been receiving considerable considerafion recently, because\nof the impact of plasfic lifter on the marine environment. The results of this paper will contribute\nsignificantly to the design of a future internafional mission that may also tackle other pollufion issues at\nsea.\nThe analysis of the literature in the field and of the state of the art is complete and relevant.\nThe work is described in a clear, complete, and reproducible way. Data analysis, interpretafion, and\nconclusions appear conducted with a sound methodology.\nThe balance between the main paper and the large supplementary materials documents is in general\nappropriate, but the paper is not fully self-contained unless the supplementary material is taken into\naccount. The lafter is very wide and the secfions are not perfectly consequent to each other. This is\nunderstandable because this document is not meant to be structured as a paper, but rather as a\ncontainer for data that are not meant to be read at the same level of depth by all readers. However,\nmore direct coordinafion of the paper and the supplementary material is desirable. All the\nsupplementary material should be essenfially summarized and referenced in the paper, and be\ncompletely funcfional to adding detail and in-depth discussion to the corresponding secfions of the\npaper. This is not always the case, and such coordinafion might be improved to facilitate readability and\nretrieval of informafion.\nA specific instance of this concern is related to the choice of the bands indicated for the mission. I think\nthat this issue, which is very important and central to the scope of the paper, is treated in the paper too\nrapidly, without an explanafion of the methodology used for the choice of the bands and the bandwidth\nof each, which is to be found in the supplementary materials in at least two different secfions, and with\narguments not completely developed.\nAnother issue that I would like to highlight concerns the choice of Lifter Windrows as a proxy for\nlifter concentrafion. This choice is clearly and convincingly discussed in the paper and it is understandable that\nvalidafion of the relafion of LWD to the actual concentrafion of lifter is very difficult due to the lack of\nsufficient ground truth. However, I think that this issue of validafion should be discussed at greater\nlength, and the feasibility of obtaining relevant ground truth assessed. Even if the correlafion of LWD and\nactual lifter density is very reasonable, it might be argued that different wind and currents condifions\nmight cause LWs to build up differently (or not at all) even with the same lifter quanfifies.\nLastly, I would ask the authors to comment in the paper on the possible use of data from the PRISMA\nmission to enhance the significance of their study. PRISMA hyperspectral data might be an important\nsource of validafion of the choice of the bands and bandwidths as discussed in this paper.\nreviewed by Marco Balsi"}, "author_response": "POINT-BY-POINT REPLY FOR THE REVIEW\nREVIEWER #1 (Remarks to the Author):\nGeneral evaluation: This manuscript presents interesting and potentially valuable results based\non a new approach of detecting windrows from space, and confirming via spectral profiling that\nthese windrows contain floating anthropogenic litter, in particular plastics. The general findings\nappear reasonable, namely more windrows in coastal regions, more litter windrows (LW) close\nto coastal sources and more abundant after rainfalls. Also the comparison of LWs with Plastic\nInputs (PIs) appears suitable and generates very useful results (it basically is the Proof of\nConcept). I congratulate the authors for the idea and also for the immense work with image\nprocessing and the data analysis.\nWe thank the Reviewer for such a positive assessment and for acknowledging the dedication\nand effort that the group of authors has put into this work.\nWhile the key results (figures 1-7) are impressive and relatively easy to follow, the structure of\nthe manuscript is confusing and especially the concepts presented in the text require revision and\nfocusing. The authors present a number of constructs (e.g. the “PoC detection processor”) that\nare confusing in their logical conception and presentation. For example, they introduce the\n“Proof of Concept”, which certainly is a very useful and valid approach (and the PoC itself is not\nthe problem). In the present context, the PoC could mean that they propose a stepwise approach\nthat passes through the remote detection of windrows, then the spectral profiling of plastic litter\n(which identifies among all the detected windrows those that indeed contain large quantities of\nplastic litter and results in the LWs), and via quantification of size (length and area) of these LW,\nthey then determine the LWD (in ppm, which seems to be pretty much “m2 per km2”). In a next\nstep they then correlate these LWDs with estimated Plastic litter Inputs (PI), which is based on\ninput source (=rivers?), runoff, and population density. They find a reasonably good correlation\nbetween LWD and PI, which, of course, is not perfect, but they identify the areas where important\ndeviations from the overall correlation are found, and they provide reasonable arguments to\nexplain these deviations (more local processes that cause these deviations). All this together\n(correlation between LWD & PI; with explanation of major deviations) then could be considered\nas a first “Proof of Concept” for this new approach. This seems to be the underlying “concept”\n(sic) of this study, and the main results follow this line of argumentation and testing of the PoC.\nHowever, the authors then create substantial confusion by introducing the “PoC detection\nprocessor”, which is a very odd construct. The PoC is here apparently converted to a specific\n“detector” that uses different spectral approaches and computing analysis steps to identify\nindividual LWs. This (detection of Ws and identification of LW) is certainly an important part of\nthis approach, but the linguistic construct of a “PoC detection processor” is very confusing and I\nstrongly recommend to find a different construct for this, because the PoC is the umbrella\nconcept that embeds the entire study: if the authors can show that their approach provides\ndecent results that are useful to identify areas with high amounts of PL (accumulated in LW),\nthen this is a reasonable Proof of Concept for their approach. In your analytical approach you\ncertainly can have a detection processor, but I strongly recommend to give this a different\nidentify than “PoC detection processor”.\nThe Reviewer's comments are entirely correct. We recognise that the sequence of steps\ndescribed in the previous version was not the most appropriate. The line of argumentation did\nnot follow the sequence of steps in our work, and this fact indeed can lead to misunderstanding.\nThe temporal sequence of tasks in our work was not: remote detection of windrows =>\nquantification of LW and LWD => relation with environmental/anthropogenic drivers =>\nproposal of optimal sensor (as described in the initial version). The work started in 2017 with\nthe design of an optimal mission concept, based on the spectral profiling of marine plastic\ndebris. It was later (2021) when we rolled out the proof-of-concept to test the feasibility and\nusefulness of the mission concept by implementing it with best available technology on a real-\nworld case study. This was done after realizing that Sentinel-2 had a partial compatibility with\nthe required ideal instrument. With this PoC, we aimed to know whether LW abundance is\nsufficient to capture spatio-temporal patterns at any scale, explore the meaning of the LW\nproxy, and evaluate potential usefulness for addressing marine litter pollution.\nACTIONS:\n1- The new version presents a more logical arrangement of the work. Now, it starts with the\ndefinition of the optimal mission concept (previously placed in a final section). Then we describe\nhow, from this optimal concept, we derive a suboptimal approximation based on the in-orbit\nSentinel-2.\n2- We have included a simplified version of the former Fig. S1 into the main body (as suggested\nbelow). We believe that the inclusion of this figure at the beginning of the manuscript (Fig. 1 in\nthe new version) is an important aid for the comprehensive understanding of the stepwise\napproach. Below, we show this new figure:\nFig. 1 | Roadmap followed for the definition of the optimal mission concept (left) and for the\nproof of concept (right). “Min. coverage” refers to the minimum fraction of plastic surface coverage\nrequired to identify a pixel with a true positive, estimated for both optimal mission and the proof of\nconcept. An extended version of the workflow can be found in Fig. S1.\n3\n3- The construct \"PoC detection processor\" has been removed in the new version of the\nmanuscript, as suggested by the Reviewer. “PoC detection processor” was replaced by\n“detector”.\nAnother issue is the fact that the current approach only identifies LWs even though it seems that\nthe authors might also be able to identify Windrows (Ws). I have to admit that I did not read the\nentire 137 pages of the electronic supplement, but I glanced through it and looked at different\nsections in order to better understand the overall approach. On P. 29 of the supplement, the\nauthors say “To ensure our focus on LWs, we combine the spectral index with a contextual filter\nbased on the geometry of such structures (section 2.1.8), and a further filtering was applied\nduring post-processing (sections 2.2.2 and 2.3.3) to ensure that only LWs with high ML density\nwere preserved for the PoC analysis.” In the context of this study, it seems very important to\nunderstand where windrows are and which of these windrows really contain ML, and maybe\neven how much ML. As discussed in some of my comments in the annotated manuscript, I suspect\nthat the rainfalls flush a lot of natural vegetation into the sea, and that initially the Ws have a\nlow proportion of ML, but that over time the proportion of ML increases (because the plastic\nlitter persists while natural vegetation disintegrates and sinks). I understand that this question\nis not the goal of this study, but the proportion of Ws that have ML and therefore are classified\nas LWs does seem highly relevant and should be explored and presented if at all possible.\nOur detector is able to identify pixels with plastic-alike spectra (using the WSI spectral index,\nSI Section 2.1.7). Along with the windrows, we can identify patches with plastic-alike pixels and\nsections of the windrow with no apparent presence of plastic (see Fig. 2a). To generate a positive\ndetection of a litter windrow, the pixels matching the plastic-alike spectra must make up a\nfilament of considerable length (contextual filter). However, we did not keep those windrows\nthat are not positive with the spectral index WSI that we designed. Many of them are not even\ndetected with WSI, but we see the relevance of what the Reviewer proposes. This information\nmay be of great interest to biological oceanographers and other researchers. Windrows without\nlitter were not our goal, and unfortunately, they were not counted here. We take note of this\ninteresting idea and will try to include this functionality in the next development of the detector\nand for new implementations.\nWe acknowledge that the Supplementary Information (SI) is extensive, and that some sections\nare quite technical. However, we believe it was necessary to include this extensive document to\ngive the possibility to replicate and improve our advances. Below, we pick out some points from\nSI that refer to the comments raised by the Reviewer.\nRegarding the Reviewer's question on the content of the windrows and potential quantification\nof marine litter, this matter is a conundrum for the Earth Observation community yet. The main\nreason why we need a dedicated satellite mission relies on various factors, being the lack of\ndiscrimination capability the main one. Up to date, very few existing missions have spectral\nresolution enough to theoretically describe floating plastic and separate materials within a\nwindrow pixel. In particular, hyperspectral missions should have a much better chance, but their\nworse spatial resolution means they have to do better in terms of signal-to-noise to achieve\ndetection. A reduced number of pixels makes it much harder to differentiate a windrow from\n4\nnoise. The noise can be caused by instrument artifacts, observing conditions like clouds, glint,\nor closeness to the edges of the range of detection. Our technological assessment during the\ndefinition of the optimal mission concept, led us to conclude that the threshold value of spatial\nresolution to describe properly a windrow is 10 m/pixel, and ideally, we should try to get the\nvalue down up to 5m or even 3m (current hyperspectral missions are in 30m/pixel). The\nchallenge, however, is to get sufficient signal-to-noise at these resolutions, the reason why 10m\nis safer and closer to what can be done with current technologies.\nWith that in mind, from all the flying non-commercial missions, only Sentinel-2 meets the\nrequirements of spatial resolution, and several of its bands are compatible with those derived\nin the mission concept. It is not perfect though (otherwise we would not need a specific mission),\nand the number of compatible bands is too low for a complete discrimination. Additionally,\nSentinel-2 has an uneven spatial sampling that complicates things. The work from Hu (2022, ref.\n21) is a good read to properly understand the challenges and limitations of Sentinel-2 for marine\nlitter, but in a nutshell, Hu (2022) concludes that the possibility to separate marine litter from\nother non-photosynthetically active materials with this satellite is quite limited. This does not\nmean it is not possible, but so far, none of the published papers on the topic has presented a\nsolid methodology for the spectral unmixing required to do this distinction within the non-\nphotosynthetic floating material fraction.\nThe reason this spectral unmixing is necessary is that marine litter submesoscale accumulations\nof floating matter will rarely be composed exclusively of marine litter or plastics. In most cases,\nwe will be talking about a mixture of things. Accompanying materials are often driftwood, leaves\nand the likes, particularly after rainfall, as indicated correctly by the Reviewer. Another common\naccompanying matter is metastable seafoam. The accompanying materials impact the apparent\ncolor of the pixel (land-based materials tend to be brown; seafoam tends to be white/yellowish),\nwhich means relying on “color” for separation is prone to errors. It is in the NIR and SWIR bands\nwhere we have more discrimination options for marine litter, being this one of the main reasons\ndriving the design of the WSI index. We, indeed, explored this issue in our study.\nIn SI Section 2.1.7, we introduce the “Normalized Spectral Index Confusion Matrix”. The idea\nbehind this method is to study relationships between the spectral bands of a sensor for a set of\ntarget materials (or pixel classes) and help to identify which pairs of bands contain the maximum\ninformation about one spectral class, and how those pairs of bands behave in other materials.\nOnly the pairs that maximize information and reduce the confusion between materials shall be\nselected. This is how we isolated the target bands in Sentinel-2, which were then compared with\nthose identified for the ideal sensor.\nWhen computing Normalized Spectral Index Confusion Matrix using selected patches of\nSentinel-2 pixels for different spectral classes, marine litter provides a specific signature vs. the\nother classes, with some specific bands being better than others. We explored those bands to\ncreate our WSI spectral index. This method does not guarantee a 100% successful distinction,\nbut allows us to choose the appropriate bands to maximize the chances that a detection\ncorresponds to a given pixel class and not to another. The thresholding method applied to WSI\nobeys the need to establish a differentiation between what is considered as marine litter and\nwhat is not.\nSupplementary material, we introduced some validation tests of this method, using observed\nLWs with visual confirmation from local observers (MARIDA database includes some of those),\nalong with a technique to convert WSI values into marine litter fractions. The results of this\nmethod are presented in an example in the Supplementary Figure 9. There, readers can see how\nit is possible to calibrate WSI to produce an estimation of fraction. However, this method is not\na spectral separation or unmixing, but rather an estimation of the amount of material falling\nwithin the general marine litter class present in a pixel, taking into account that this class embeds\nnot only litter, but also some of the accompanying materials (seaweeds, driftwood, seafoam) in\nunknown proportions. Because the proposed method is context-dependent and has to be\ncalibrated for each windrow, it is not possible to train it using some well-known LWs to estimate\nthe composition of other LWs detected by the satellite. Thus, we get limited in our results to\nreport the positive pixels. We used this method on the well-known LWs to identify the lower\nlimit detection threshold for Sentinel-2 sensor (20%). Future work may go in this direction, in\norder to refine the results and provide further information.\nThus, the combination of WSI with a threshold system is what allows our method to isolate pixels\nwhere there is most likely to be marine litter and in particular plastic, due to the use of plastic\nspectral signatures in the definition of the WSI index. But for now, we cannot quantify the exact\ncomposition of such marine litter. The post-filtering layers we added and the manual screening\nwe also did are intended to remove filaments that are not what we are looking for. In particular,\nthe use of NDVI and NDWI in the post-filtering (SI Section 2.2.2) not only removes “artifacts”,\nbut also vegetation-like, or photosynthetically active filaments by construction of the filter\nranges. This means that we only retain windrows that are mostly composed of non-\nphotosynthetic materials.\nACTIONS:\n1. Our work advances the spectral detection method of plastic debris by using, for the first time,\nan infrared-based spectral index (WSI) and other methods to remove artifacts (e.g. windrow\ncomposed of vegetation-like material). In addition, we found a reasonably good correlation of\nLWD with plastic inputs and basin-scale data. However, we agree that we must make it clear\nthat our detections should be understood as aggregations containing varying fractions of\nanthropogenic and natural debris. This limitation arises from the technical constraints of the\nsuboptimal sensor used (S2-MSI), and it is common to all papers dealing with plastic detection\nthrough this sensor, although this has not always been made clear. In the new version of the\nmanuscript, we have removed any doubt that might lead readers to believe that our detections\ncorrespond unequivocally to plastic. The “Optimal concept and proof of concept” section now\nreads (new text is underlined):\n“While the spectral detection was targeted on plastic, detections must be considered here as\nML aggregations containing varying fractions of both anthropogenic and natural debris, due to\nthe known limitations of the S2-MSI21. Eventually, with dedicated spectral bands (Table 1), it will\nbe possible to disentangle the contributions of the main litter components (e.g., plastic, weeds,\ndriftwood, metastable seafoam) to the remotely-sensed reflectance and estimate plastic\nconcentrations per pixel.”\nthe annotated version of the revised manuscript):\n“Nevertheless, in the Mediterranean Sea, the majority of all floating items are plastics (> 75%,\naccounting for both anthropogenic and natural) and the abundance of natural debris has been\nfound to correlate positively with plastic concentrations22,23. Therefore, the ROI attributes led us\nto expect a potentially significant relationship between our PoC detections and plastic\ndynamics”.\n2. Following the suggestions of Reviewer #2, we have included a new paragraph in the last\nsection of the manuscript describing how to build on the progress of this work to increase the\ncollection of field data on LWs, and thus advance the determination of the contributions of their\nmain litter components. It is a Reviewer #2's suggestion but we believe it fits well with Reviewer\n#1's commentary as well. This new paragraph reads:\n“Next steps should also advance on the ground truthing (Fig. 1). The current matching of satellite\ndetections and field observations is limited to fake targets (artificial LWs)15,20 and reports of\ndense LW sightings14 (Figs. S9 and S11). Filling this gap requires targeted and coordinated\nresearch on the LW phenomenon and the combination of several approaches. Fake targets are\nan effective approach, which might be further extended to different LW compositions and\nenvironmental conditions. Matching detections of natural LWs and field measurements is a more\nchallenging task due to the difficulty of finding LWs from ship surveys13. We can now take\nadvantage of satellites to address areas and periods of common LW formation (Fig. 5).\nComplementary observation platforms with larger coverage area than ships (e.g. long-range\ndrones, aircrafts) could also significantly increase LW findings27. Additional inputs for calibration\nand validation can be gained from the ocean-wide spreading of animal-borne cameras47,\nparticularly from species using windrows as feeding spots. Citizen science could also contribute\nwith opportunistic observations, whereas from leisure vessels, fishing, or commercial ships.\nFinally, another pivotal approach stems from the overlapping of S2-MSI detections with\nobservations from medium-to-high resolution hyperspectral satellites48.”\n3. We have improved the linkage between the main manuscript and the key points of\nSupplementary Information, in order to facilitate the use of this material for more specific\nquestions.\nIn general, the text sometimes is very difficult to follow, which is also due to the fact that the\nauthors use a lot of abbreviations, not all of which are intuitive. I would advise to use fewer\nabbreviations and only focus on the most important and most frequently used ones. Similarly,\nconcentrating the main text on the key results (figures 1 – 7) will substantially improve the\nmanuscript.\nAgain, the Reviewer is right in his comment. The work was quite long and dense, and indeed a\nsimplification of the text together with a shortening have significantly contributed to its\nimprovement.\nACTIONS:\n1. We have reduced the total number of abbreviations by 20%, focusing on the most important\nand most frequently used abbreviations. This reduction of abbreviations has been even more\nintense in the main manuscript (half of the abbreviations (11) have been deleted in the revised\nversion of the main manuscript). As suggested in the annotated manuscript, we have also\nwritten out the acronyms in the figure captions.\n2. We have considerably reduced the text of the main manuscript, moving the section on the\nNorthern Adriatic and the exploration of possible applications to the SI (as suggested by the\nReviewer in comments below).\n3. We have reduced the number of figures. In the new version, figures 1 to 7 remain (as\nsuggested by the Reviewer), and figure 9 was moved to Supplementary Information. Figure 8\nwas a variant of the current figure 4, so we have removed it and included a simplified version in\nan inset in figure 4. These changes have resulted in a more synthesized, orderly and logical\npresentation of the figures. The new figure 4 (with “b” inset) is shown below:\n1) Title: ok\n2) Abstract: ok\n3) Introduction: ok, motivates the present study well.\n5) Methods: it would be good to add a conceptual figure in the manuscript that illustrates the\nkey steps in this new approach; the figures in the supplement are very extensive and detailed,\nand a synthetic overview figure could be useful (while maintaining the detailed figures in the\nsupplement).\nnecessary for an easier understanding of the work. Thanks for this observation.\nACTION: The SI figure with the synthetic overview referred to by the Reviewer is the previous\nFig. S1. This figure remains in SI, but a simplified version is now shown in the revised version of\nthe main manuscript. This new figure is shown in a reply above (new Fig. 1).\n6) Results & Discussion: the initial results are very interesting and nicely illustrate the stepwise\napproach and findings, but starting with figure 8, it seems that not all that info is necessary. In\nfact, starting with the subchapter “The case of the North Adriatic”, it seems that information\nbecomes too detailed and some aspects are repetitive. It seems that the Adriatic subchapter, if\nit were to be maintained, should be moved to the electronic supplement. The subchapters about\nthe optimal mission concept and then the scenario for research and management go beyond the\nprincipal goals of this study and should actually be drastically condensed and converted into a\nvery short and concise conclusion chapter. The current length of this results & discussion section\nsubstantially dilutes the overall impact of this new approach.\nAs commented above, we have kept the stepwise presentation up to figure 7, and moved to SI\nthe sections and figures indicated by the Reviewer. These changes have resulted in a shortening\nof the main paper to focus on the most relevant findings.\nACTIONS:\n1. Figure 9 was moved to Supplementary Information, as suggested. We considered that figure\n8 was necessary for a better understanding. So, we have included a simplified version in an inset\nin figure 4. The new figure 4 is shown in the responses above.\n2. The section about the “optimal mission concept”, once simplified and merged with the former\n\"PoC detection of marine litter windrows\" section, has been moved to the beginning of the\nmanuscript. The section resulting from this merging is entitled: \"Optimal concept and proof of\nconcept\". This initial section now provides a synthetic overview of the work in a more logical\norder, which we believe facilitates the understanding of the reasoning behind the work.\n3. The section \"The case of the North Adriatic\" has been moved to SI (Section S4), as suggested.\n4. The text regarding the exploration of practical application cases, in the section \"A new\nscenario for research and management\", has also been moved to SI, as suggested. As a result,\nthis section has been shortened and simplified. The long paragraph dealing with the exploration\nof application cases has been reduced to a sentence linking to SI. This sentence reads:\n“Using real case scenarios, Supplementary Information illustrates some applications, such as the\nidentification of pollution hotspots, shipping-related sources of litter, long-term evaluation of\naction plans or better targeting of clean-up efforts (SI Section 4, Fig. S33).”\n7) References: I have not checked whether all references are cited in the text and vice versa, but\nI feel that the authors could possibly draw a bit more on the Sargassum studies, which have\n9\nfollowed a somewhat similar approach, for example (and several other papers): Dierssen, H.M.,\nChlus, A. and Russell, B., 2015. Hyperspectral discrimination of floating mats of seagrass wrack\nand the macroalgae Sargassum in coastal waters of Greater Florida Bay using airborne remote\nsensing. Remote Sensing of Environment, 167, pp.247-258.\nWe have included the references suggested by both Reviewers, and we have checked the\nreferences cited in the text and vice versa. We thank the Reviewers for their suggestions of\narticles, especially the relevant article by Tramoy et al. (suggested in the annotated manuscript),\nwhich provides field evidence of the strong input of litter recorded by satellite in southern\nFrance during the spring of 2018. On the other hand, the shortening of the paper has resulted\nin a transfer of references from the main manuscript to the SI.\nACTION: The following references have been added to the revised version of the main\nmanuscript:\n- Tramoy, R., Blin, E., Poitou, I., Noûs, C., Tassin, B. and Gasperi, J., 2022. Riverine litter in\na small urban river in Marseille, France: Plastic load and management challenges. Waste\nManag. 140, 154-163 (2022).\n- Laverre, M. et al. Heavy rains control the floating macroplastic inputs into the sea from\ncoastal Mediterranean rivers: A case study on the Têt River (NW Mediterranean Sea).\nSci. Total Environ. 877, 162733 (2023).\n- Korshenko, E., Zhurbas, V., Osadchiev, A. & Belyakova, P. Fate of river-borne floating\nlitter during the flooding event in the northeastern part of the Black Sea in October\n2018. Mar Pollut Bull. 160: 111678 (2020)\n- Nishizawa, B., Thiebot, JB., Sato, F. et al. Mapping marine debris encountered by\nalbatrosses tracked over oceanic waters. Sci. Rep. 11, 10944 (2021).\n- Corbari, L., Capodici, F., Ciraolo, G., & Topouzelis, K. Marine plastic detection using\nPRISMA hyperspectral satellite imagery in a controlled environment. Int. J. Remote Sens.\n44, 6845-6859 (2023).\nThe following reference have been added to the new version of SI (as suggested by the\nReviewer):\n- Dierssen, H.M., Chlus, A., & Russell, B. Hyperspectral discrimination of floating mats of\nseagrass wrack and the macroalgae Sargassum in coastal waters of Greater Florida Bay\nusing airborne remote sensing. Remote Sens. Environ. 167, 247-258 (2015).\nIn summary, I think that this could become an important contribution, but it will require major\nrevision and careful focusing of the text. This will likely mean a lot of additional work but I think\nthat it is mainly careful crafting of the text, and so it should be doable. I encourage the authors\nto take on this task, because it will ensure the future impact of their paper and approach.\nWe are sincerely very satisfied with the changes suggested by the Reviewers. Thanks for the\ncontribution.\nIn case of any questions about my comments, I invite the authors to contact me directly.\nSincerely, Martin Thiel\nFacultad de Ciencias del Mar, Universidad Católica del Norte, Larrondo 1281, Coquimbo, Chile\nOther minor ACTIONS in response to the suggestions included in the annotated version of the\nmanuscript included:\n1. In the new version of the main manuscript, we have written the years (instead of “January\nYear”) on x-axis in the figures (Figs. 7 and 8 in the revised version of the manuscript).\n2. In the new version of “Introduction”, we have clarified that windrows can also be referred to\nas \"filaments\" (as suggested in the annotated version of the revised manuscript). Introduction\nnow reads (new text underlined):\n“...the metre-sized aggregations of floating debris, the so-called surface slicks, streaks,\nfilaments or litter windrows”\nThe paper reports on a very wide study aimed at defining specifications for an earth observation\nsatellite mission with the purpose of detecting plastic litter at sea. After motivating the use of a\nproxy (litter windrows) for detections, rather than aiming at the direct detection of scattered\nplastic items, the authors study a very large dataset covering the whole Mediterranean Sea for\na long period of time, taken from Sentinel data, to make a convincing feasibility study for the\nmission. Based on simulation of the reflectance properties of water, and of natural and artificial\nmaterials (specifically, the plastic polymers that are most represented in marine litter), at\nreasonable concentrations for real-world scenarios, an evaluation of the SNR obtained at\nsatellite vs. the wavelength of received radiation (from UV to SWIR) is obtained. The most\nrelevant bands according to the SNR for plastics, as well as for confounding materials, are\nchosen, discussing in this way the specification of a \"super-spectral\" sensor system for the\nmission envisioned.\nThis work is very relevant to a task that has been receiving considerable consideration recently,\nbecause of the impact of plastic litter on the marine environment. The results of this paper will\ncontribute significantly to the design of a future international mission that may also tackle other\npollution issues at sea.\nWe are grateful to Reviewer #2 for the thorough reading and appreciation of our work, as well\nas for the comments provided, all of which were very helpful in improving the manuscript. The\nscope of the manuscript is broad, so we are happy that the main ideas we wanted to convey are\nwell captured by an external reader.\nThe analysis of the literature in the field and of the state of the art is complete and relevant.\nWe thank the Reviewer for checking the provided references and the assessment on the state\nof the art.\nThe work is described in a clear, complete, and reproducible way. Data analysis, interpretation,\nand conclusions appear conducted with a sound methodology.\nWe appreciate such positive comments.\nThe balance between the main paper and the large supplementary materials documents is in\ngeneral appropriate, but the paper is not fully self-contained unless the supplementary material\nis taken into account. The latter is very wide and the sections are not perfectly consequent to each other. This is\nunderstandable because this document is not meant to be structured as a paper, but rather as a\ncontainer for data that are not meant to be read at the same level of depth by all readers. However,\nmore direct coordination of the paper and the supplementary material is desirable. All the\nsupplementary material should be essentially summarized and referenced in the paper, and be\ncompletely functional to adding detail and in-depth discussion to the corresponding sections of the\npaper. This is not always the case, and such coordination might be improved to facilitate readability and\nretrieval of information.\nmanuscript is the result of several years of work, so for the sake of reproducibility and\ntransparency, we were obliged to present much of the methods, experiments and analyses in\nan extensive and detailed supplementary document.\nThe Reviewer is right: we intended the document to be consultative and not to be read\nsystematically, putting it in the order that the different parts of the study were done, which is\nwhy sometimes it may not look consequential. However, only in this way can one follow the\ndecision-making process and how we converged towards the final approach.\nWe agree on the importance of improving the linkage between the main paper and the sections\nof the Supplemental Information, so readers can be guided to the right sections if they wish for\ndetails on the methods or further discussion.\nACTIONS:\n1. Based on the comments of Reviewer #1, we have changed the order in which ideas are\npresented in the manuscript. The revised version of the main manuscript now starts with the\ndefinition of the optimal mission concept and then we describe how we derive a suboptimal\napproximation based on the in-orbit Sentinel-2. This change has resulted in a more logical\narrangement of the work, adjusted to the real-time sequence of steps followed in our work.\nImportantly, now the revised version of the manuscript follows a structure parallel to that in SI,\nresulting in an easier linkage between the two documents.\n2. We have revised throughout the manuscript the linkage to the Supplementary Information,\nwhich we believe has now been considerably improved (referencing highlighted in yellow in the\nrevised version of the manuscript with tracked changes). To avoid impeding a smooth reading\nof the main text, though, we have tried to find a compromise between the number of times a\ncross-reference was introduced in the text and an easier reading. We hope the additions made\nin this regard are found satisfactory.\nA specific instance of this concern is related to the choice of the bands indicated for the mission.\nI think that this issue, which is very important and central to the scope of the paper, is treated in\nthe paper too rapidly, without an explanation of the methodology used for the choice of the\nbands and the bandwidth of each, which is to be found in the supplementary materials in at least\ntwo different sections, and with arguments not completely developed.\nWe agree that we have addressed this issue too succinctly in the main paper. The reason was\nthe space constraints as well as that we decided to put the main focus on the proof of concept.\nHowever, it is true that the optimal mission concept is also very important and more attention\nshould be given. As Referee #1 asked for some text reshuffling and cleaning on the main\nmanuscript, we have taken the chance to introduce in the main manuscript some additional\ninformation on the band selection process and also a better link to the relevant sections of the\nSupplemental Material.\nACTIONS:\n1. As commented above, following Reviewer #1 comments, the definition of the optimal mission\nnow has a more prominent place in the manuscript, being now presented in the first section of\n13\nthe revised manuscript. Also, the new figure 1 presents a comprehensive framework of the\nwork, showing both the definition of the optimal concept and the proof of concept, and their\ninterrelationship. This new figure is shown above in the responses to Reviewer #1\n2. Although we tried not to increase the length of the manuscript (see Reviewer #1 comments),\nwe have added new text and links to SI sections regarding band selection. The revised version\nof the manuscript now reads (new text underlined):\n“...This spectral library was fed into an atmospheric radiative transfer model in order to\ndetermine spectral radiances at the top of the atmosphere. The isolation of spectral bands for\nthe sensor, and their width, was achieved by optimising the remote sensing performance (as\nsignal-to-noise ratio) with changing fractions of plastic polymers over a range of observation\nconditions (SI Sections S1.2.2 and S3.1.2, Figs. S13 to S21). A set of additional bands was\nconfigured to specifically address the quantification of other abundant floating materials (i.e.\nalgae, driftwood and seafoam) and to support atmospheric correction and cloud detection (Table\nS3). This exercise allowed us to define functionalities for a total of 23 candidate spectral bands\n(Table 1, Figs. S22 to S24).”\nAnother issue that I would like to highlight concerns the choice of Litter Windrows as a proxy for\nlitter concentration. This choice is clearly and convincingly discussed in the paper and it is\nunderstandable that validation of the relation of LWD to the actual concentration of litter is very\ndifficult due to the lack of sufficient ground truth. However, I think that this issue of validation\nshould be discussed at greater length, and the feasibility of obtaining relevant ground truth\nassessed. Even if the correlation of LWD and actual litter density is very reasonable, it might be\nargued that different wind and currents conditions might cause LWs to build up differently (or\nnot at all) even with the same litter quantities.\nWe fully agree with the Reviewer that field validation is a weak point for satellite remote sensing\nof marine litter. The problem does not impact solely this study, but overall, all published studies\nwith pragmatic approaches to existing data suffer from the same limitations. A large part of the\nreason why we conducted the Proof of Concept lies precisely in this argument: lacking enough\nground truth for a solid direct validation, focusing on the geostatistical significance of a large\ndata set of LW detections, and relating it to existing knowledge on marine litter pollution,\nseemed like the only possible way to proceed.\nThe results were highly consistent with such knowledge and with the main environmental\ndrivers involved in marine litter distribution. Nevertheless, we agree with the Reviewer that\nimproving ground truthing is an important next step, which in turn can benefit from the progress\nmade with our manuscript. The ephemeral nature of the windrows and their relatively reduced\nspatial scales make direct observation at sea very difficult. The revisiting time of many satellites,\neven if in Sentinel-2 is as short as 5 days on average, is not sufficient for tracking detected\nstructures nor facilitates having match-ups with field observations to perform direct validations.\nOur manuscript now allows us to take advantage of satellites to address areas and periods of\ncommon LW formation. In addition, we need targeted and coordinated research on the LW\nphenomenon as well as the combination of multiple approaches to increase ground truthing.\nsuggestion, and we have done so in the new version of the manuscript (see actions below).\nPossible strategies that should be put in place include:\n1. Use of fake targets, as done in Lesbos Island (Greece)\n2. Further coordination and international collaboration for field sampling, not only by\nmeans of ships but also by other means with larger coverage, such as airplanes,\nhelicopters, and long-range drones, probably all of them supported by the information\nthat can be generated by Sentinel-2. Some initiatives, such as the Ocean Scan\n(https://www.oceanscan.org/) and the IOCCG Taskforce for Remote Sensing of Marine\nLitter and Debris (https://ioccg.org/group/marine-litter-debris) could maybe foster\nthese actions.\n3. Use other missions to better study the detections done by Sentinel-2 (e.g., PRISMA,\nshould concomitant data at the right time be found).\n4. Further methodology testing using hyperspectral sensors mounted on aircrafts.\n5. Citizen science to support proper reporting of identified litter windrows at sea (e.g.,\nfurther involvement of fishermen and environmental policing agencies)\nRegarding the building of LWs, in the main manuscript, we have specifically accounted for the\nrole of wind in order to obtain more realistic estimates of litter pollution:\n“Quantitative assessment of ML pollution on a continuous scale was done through the fraction\nof sea surface area covered by LWs, referred to as litter-windrow density (LWD, in ppm; e.g., m2\nper km2). Importantly, LWD was calculated solely over the sea surface under environmental\nconditions deemed suitable for litter aggregation, i.e., for winds lower than 5 m s-1 (see\nMethods). By reducing spatio-temporal bias due to different wind conditions and, consequently,\nvarying conditions for LW formation, this wind-based constraint was intended to improve the\ncomparability of LW detections in space and time, an issue hitherto unaccounted for”.\nIn SI figure S27, we show the apparent role of wind speed on LWs, which seem to break off when\nwinds are above 5 m/s.\nSupplementary Information regarding the consideration of wind on LW detections is now more\nsuitably referenced in the revised version of the main manuscript. Moreover, we agree that the\neffect of physical forcing on LW formation must be addressed in future research, as this has not\nyet been fully disclosed by our research. This idea is now more clearly visible in the last section\nof the revised manuscript.\nIn short, we have made some small changes to the revised version of the manuscript to stress\nthe difficulty of direct validation and how we might address this gap, as well as to better frame\nthe range of validity of the work undertaken.\nACTIONS:\n1. On the need to move forward on the physical control of LW formation, we now have a single\nparagraph in the final section that addresses this issue. This single paragraph reads as follows:\n15\n“The readiness level of the present monitoring tool allows for its implementation in other ocean\nregions; however, there is still much room for improvement. S2-MSI surveys in other ocean\nregions could advance the mission concept, analysis methods and data interpretation. Current\nLWD estimates account for wind conditions, since LW formation requires not only high ML load,\nbut also favourable sea state13. Nevertheless, further refinement of LWD estimates should\nideally account, along with disruptive forces (e.g. wind mixing), for major formation forces (e.g.\ntidal forcing).”\n2. Regarding the ground truthing and how we might address this limitation, a specific paragraph\nwas introduced in the new version of the manuscript. This new paragraph reads:\n“Next steps should also advance on the ground truthing (Fig. 1). The current matching of satellite\ndetections and field observations is limited to fake targets (artificial LWs)15,20 and reports of\ndense LW sightings14 (Figs. S9 and S11). Filling this gap requires targeted and coordinated\nresearch on the LW phenomenon and the combination of several approaches. Fake targets are\nan effective approach, which might be further extended to different LW compositions and\nenvironmental conditions. Matching detections of natural LWs and field measurements is a more\nchallenging task due to the difficulty of finding LWs from ship surveys13. We can now take\nadvantage of satellites to address areas and periods of common LW formation (Fig. 5).\nComplementary observation platforms with larger coverage area than ships (e.g. long-range\ndrones, aircrafts) could also significantly increase LW findings27. Additional inputs for calibration\nand validation can be gained from the ocean-wide spreading of animal-borne cameras47,\nparticularly from species using windrows as feeding spots. Citizen science could also contribute\nwith opportunistic observations, whereas from leisure vessels, fishing, or commercial ships.\nFinally, another pivotal approach stems from the overlapping of S2-MSI detections with\nobservations from medium-to-high resolution hyperspectral satellites48.”\nLastly, I would ask the authors to comment in the paper on the possible use of data from the\nPRISMA mission to enhance the significance of their study. PRISMA hyperspectral data might be\nan important source of validation of the choice of the bands and bandwidths as discussed in this\npaper.\nIndeed, the Reviewer is correct in indicating hyperspectral missions as having the potential to\nsupport the development of a dedicated mission. PRISMA, along with other hyperspectral\nmissions such as EnMAP, have been considered during the technological feasibility phase of this\nstudy. In Section 3.2.2 of the Supplemental Information (SI), we referred to such missions and\nemphasized that \"these missions offer opportunities for testing marine litter detection methods\ndirectly in the spectral bands of interest”. We also mentioned, in the same section, their\npotential added value to support the building and testing of end-to-end simulators. We studied\nPRISMA as part of our technology evaluation to find current sensors and data that may be used\nto report marine litter (SI Table S5). We did not mention the mission, though, in the main\nmanuscript. We fully agree that, once the right bands have been identified, those could be\nexploited by existing hyperspectral sensors to test their potential for this task.\n(2023) titled: “Marine plastic detection using PRISMA hyperspectral satellite imagery in a\ncontrolled environment.”\nThe results of the paper are based on a limited set of collocations between PRISMA,\nPlanetScope, and drone spectra data over the artificial targets deployed on Lesbos Island\n(Greece) by the University of Aegean. Even under those controlled conditions, the authors found\nthat PRISMA has some limited capability, which mainly worked for detection and not as much\nfor quantification, an objective we endorse for a future dedicated mission. The authors did not\nmanage to fully geolocate targets with PRISMA pixels (Fig. 5 of the publication), so validating the\nretrieved fractions vs. ground truth would be difficult. They also acknowledge that the PRISMA\nspatial resolution may be too coarse for this type of application. In contrast, they showed value\nin using spectral profiling derived from PRISMA for marine litter detection.\nIn the Supplementary Information, we demonstrate how the key aspect is the use of spectral\nanomalies, and not much the use of bulk reflectance measurements, understanding such\nanomalies as values with respect to seawater. The additional bands of a hyperspectral mission\noffer more opportunities for detecting anomalies (this is well illustrated in Corbari et al. 2023),\nbut wider bands offer better anomaly detection. We focus on this aspect since spectral\nanomalies can be related to detection and quantification, and those can be better constrained\nwhen more bands are available (i.e., better spectral resolution). However, the compromise\nneeded between spectral sampling and signal-to-noise ratio led us to conclude that an\nintermediate solution was needed. A multispectral sensor will have good bands but not in\nenough quantity. A hyperspectral sensor would have many bands but not so good (at least to\nthe required spatial resolution). The super-spectral instrument falls in between and tries to get\nthe best of both worlds as a compromise.\nAn optimal solution would require both spectral and spatial resolution, as well as sufficient\nsignal-to-noise ratio to be fully functional. Anomalies can help improve SNR by removing\nunwanted information, so they work better for hyperspectral sensors, where spectral\nuncertainties may be higher than in instruments with wider bands. Decluttering satellite spectral\ndata quite often involves reducing spatial resolution for most applications. This, in turn, forces\na better signal-to-noise ratio in the averaged pixel to trigger the detection of a sub-pixel\nstructure that would also be of smaller apparent size for the pixel size.\nConsequently, the difficulty in applying PRISMA to validation lies in the challenge of establishing\na clear linkage between ground truth data and PRISMA detections, so that the latter may be\nutilized to determine ideal bands or to test proposals. This issue is affecting most (if not all)\ncurrent satellite data that does not meet the required signal-to-noise ratio, lacks the required\nbands, or misses the needed spatial resolution for the intended application. It is for those\nreasons that, generally, using an ASD FieldSpec instrument for field measurements along with\nradiative transfer modeling seems more adequate at this stage of mission concept development.\nThis is the approach that we followed in our work. On the other hand, we agree that proper\nresearch should be done to test whether current satellite hyperspectral missions could help\ndetermine the feasibility of the proposed Mission Concept. For this, dedicated experiments are\nrequired to solve the issue of linking remote observations and ground truth data. This could be\na good part of an end-to-end simulator exercise to further develop the mission idea in the future.\nPRISMA as an example of a mission that could fulfill such a role.\nACTION: We improved the linkage of the main body with the SI sections related to the possible\nuse of hyperspectral data in the definition and validation of the optimal mission concept.\nMoreover, in the new paragraph included as action in response to the previous comment, we\nhave specifically highlighted the option here discussed and proposed by Reviewer #2. This new\nsentence reads:\n“Finally, another pivotal approach stems from the overlapping of S2-MSI detections with\nobservations from medium-to-high resolution hyperspectral satellites48.”\nReference number 48 in the new version of the manuscript corresponds specifically to PRISMA,\nas suggested, in particular to the following article:\n- Corbari, L., Capodici, F., Ciraolo, G., & Topouzelis, K. Marine plastic detection using\nPRISMA hyperspectral satellite imagery in a controlled environment. Int. J. Remote Sens.\n44, 6845-6859 (2023).\nReviewer #1 (Remarks to the Author):\ngreat job in revising the manuscript. there are a few minor edits in the annotated manuscript. I would\nrecommend to not talk about \"game changer\" because that is a very ambiguous term, and i do not think\nthat your tool on its own will be a game changer. it is a useful tool that can be applied to many different\ntasks, but the game is being changed elsewhere, namely in the polifical arena!\nbest wishes, marfin\nReviewer #2 (Remarks to the Author):\nThe paper reports on a very wide study aimed at defining specificafions for an earth observafion satellite\nmission with the purpose of detecfing plasfic lifter at sea.\nAfter mofivafing the use of a proxy (lifter windrows) for detecfions, rather than aiming at the direct\ndetecfion of scaftered plasfic items, the authors study a very large dataset covering the whole\nMediterranean Sea for a long period of fime, taken from Senfinel data, to make a convincing feasibility\nstudy for the mission.\nBased on simulafion of the reflectance properfies of water, and of natural and arfificial materials\n(specifically, the plasfic polymers that are most represented in marine lifter), at reasonable\nconcentrafions for real-world scenarios, an evaluafion of the SNR obtained at satellite vs. the wavelength\nof received radiafion (from UV to SWIR) is obtained. The most relevant bands according to the SNR for\nplasfics, as well as for confounding materials, are chosen, discussing in this way the specificafion of a\n\"super-spectral\" sensor system for the mission envisioned.\nThis work is very relevant to a task that has been receiving considerable considerafion recently, because\nof the impact of plasfic lifter on the marine environment. The results of this paper will contribute\nsignificantly to the design of a future internafional mission that may also tackle other pollufion issues at\nsea.\nThe analysis of the literature in the field and of the state of the art is complete and relevant.\nThe work is described in a clear, complete, and reproducible way. A very large supplementary document\ncontains details covering all aspects of the work. Data analysis, interpretafion, and conclusions appear\nconducted with a sound methodology.\nThe authors reviewed the papers according to my concerns about the first version submifted, in a\ncompletely safisfying way.\nJust a very minor note: please check the units for kinefic energy in figs. 3 and 4.\nNature Communications\n26th April 2024\nSubject: Nature Communications Ms. NCOMMS-23-59779 \"Proof of concept for a new sensor\nto monitor marine litter from space”. Reply to Reviewers.\nDear Reviewers,\nThank you for this new review of our manuscript. We are very grateful for your comments and\nsuggestions. The review process has greatly improved the quality of the manuscript. In this new\nversion, we have adjusted the manuscript to the style established by the Nature\nCommunications guidelines.\nWe reproduce below the Reviewers' comments (in blue) and provide the corresponding\nresponse.\nReviewer #1 (Remarks to the Author):\nGreat job in revising the manuscript. There are a few minor edits in the annotated manuscript.\nI would recommend to not talk about \"game changer\" because that is a very ambiguous term,\nand I do not think that your tool on its own will be a game changer. it is a useful tool that can be\napplied to many different tasks, but the game is being changed elsewhere, namely in the\npoliticalarena!\nResponse: We thank Reviewer #1 for the thorough review of our work. The comments provided\nthroughout this review process have been of great help in improving our manuscript.\nWe have discussed carefully with the co-authors the suggestion to replace \"game changer\" with\n\"tool\". We have finally opted to keep the term \"game changer\". We fully believe that the\nincorporation of satellite technology into the \"game\" of marine debris monitoring can be\nregarded, based on the results shown in the manuscript, as a game changer.\nReviewer #2 (Remarks to the Author):\nThe paper reports on a very wide study aimed at defining specifications for an earth observation\nsatellite mission with the purpose of detecting plastic litter at sea.\nAfter motivating the use of a proxy (litter windrows) for detections, rather than aiming at the\ndirect detection of scattered plastic items, the authors study a very large dataset covering the\nwhole Mediterranean Sea for a long period of time, taken from Sentinel data, to make a\nconvincing feasibility study for the mission.\nBased on simulation of the reflectance properties of water, and of natural and artificial\nmaterials (specifically, the plastic polymers that are most represented in marine litter), at\nreasonable concentrations for real-world scenarios, an evaluation of the SNR obtained at\nsatellite vs. the wavelength of received radiation (from UV to SWIR) is obtained. The most\nrelevant bands according to the SNR for plastics, as well as for confounding materials, are\n1\nchosen, discussing in this way the specification of a \"super-spectral\" sensor system for the\nmission envisioned.\nThis work is very relevant to a task that has been receiving considerable consideration\nrecently, because of the impact of plastic litter on the marine environment. The results of this\npaper will contribute significantly to the design of a future international mission that may also\ntackle other pollution issues at sea.\nThe analysis of the literature in the field and of the state of the art is complete and relevant.\nThe work is described in a clear, complete, and reproducible way. A very large supplementary\ndocument contains details covering all aspects of the work. Data analysis, interpretation, and\nconclusions appear conducted with a sound methodology. The authors reviewed the papers\naccording to my concerns about the first version submitted, in a completely satisfying way.\nJust a very minor note: please check the units for kinetic energy in figs. 3 and 4.\nResponse: We thank the Reviewer for such a positive assessment.\nWe have checked the units for kinetic energy in figs. 3 and 4. They are correct.\nWe hope that you will consider this new version of the manuscript suitable for publication.\nYours sincerely,\nAndrés Cózar, Manuel Arias and co-authors\n2"}]

Decision letter