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Towards the UN SDG14.1.1b plastic debris density indicator from Earth Observation

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Marine Litter (ML) is one of the challenges identified in the United Nations Triple Planetary Crisis. ML has a significant economic impact and poses a threat to aquatic biodiversity (Richardson et al., 2023). One of the main challenges for managing this ML environmental crisis is a lack of scientific evidence-based information on the plastic leakage budget from source-to-sink in all aquatic environments, due to limited monitoring capabilities (Haarr et al., 2022; UNEP, 2016). Consequently, one of the resolutions following the United Nations Environment Assembly (UNEA) was on the importance of including remote sensing, when possible and pertinent, as a priority complementary tool among the current monitoring strategies (UNEP, 2019).United Nations Environment Programme (UNEP) has been at the forefront of promoting and establishing quantitative indicators to support member states in monitoring environmental health by means of the Sustainable Development Goals (SDGs). SDG 14 - Life below water - sets the aim to conserve and sustainably use the oceans, seas and marine resources. SDG target 14.1 aims to achieve, by 2025, the prevention and significant reduction of marine pollution of all kinds and specially from land-based activities, which includes ML, as the sub-indicator 14.1.1b plastic debris density (UNEP, 2021). This indicator is expected to be relevant for the Global Plastic Treaty. Earth Observation data has been recommended as the primary source of data to derive SDG 14.1.1b sub-indicators (UNEP, 2021).Remote sensing of aquatic litter is an emerging area of R&D that has been going through a rapid evolution over the last few years (GIZ, 2023; Maximenko et al., 2016). The Copernicus Sentinel-3 mission, carrying the Ocean and Land Colour Imager (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) has been recommended by UNEP as a potential data source for relevant plastic patches data in international waters. The capabilities of monitoring ML using Sentinel-3 are yet to be well documented, in contrast with efforts that have been done with the Copernicus Sentinel-2 Multi Spectral Instrument (MSI), which has received wide attention and demonstrated promising findings (Cózar & Arias et al., 2024; Lavender, 2022; Topouzelis et al., 2019). However, Sentinel-2 has a limited geographic coverage, restricted to within 100 km from most coastlines covering part of member state exclusive economic zone (EEZ, ~370.4 km), and a signal-to-noise ratio (SNR) not optimized for marine observations.The scope of this study was to review and evaluate EO technologies that have the potential to address the data needs for deriving SDG 14.1.1b sub-indicators. We conducted an extensive assessment that involved mission-level trade-off and Strengths, Weaknesses, Opportunities and Threats (SWOT) analyses. The research objective was to explore the suitability of the currently available EO sensors for reporting on ML in international waters. A candidate EO-based system is also recommended regarding a sensor or set of sensors that could yield the best level of descriptors. The analysis also led to identifying promising processing techniques that may respond to the expectations of SDG 14.1.1b sub-indicators. Exact capabilities and performance are expected to be assessed in future research.
Title: Towards the UN SDG14.1.1b plastic debris density indicator from Earth Observation
Description:
Marine Litter (ML) is one of the challenges identified in the United Nations Triple Planetary Crisis.
ML has a significant economic impact and poses a threat to aquatic biodiversity (Richardson et al.
, 2023).
One of the main challenges for managing this ML environmental crisis is a lack of scientific evidence-based information on the plastic leakage budget from source-to-sink in all aquatic environments, due to limited monitoring capabilities (Haarr et al.
, 2022; UNEP, 2016).
Consequently, one of the resolutions following the United Nations Environment Assembly (UNEA) was on the importance of including remote sensing, when possible and pertinent, as a priority complementary tool among the current monitoring strategies (UNEP, 2019).
United Nations Environment Programme (UNEP) has been at the forefront of promoting and establishing quantitative indicators to support member states in monitoring environmental health by means of the Sustainable Development Goals (SDGs).
SDG 14 - Life below water - sets the aim to conserve and sustainably use the oceans, seas and marine resources.
SDG target 14.
1 aims to achieve, by 2025, the prevention and significant reduction of marine pollution of all kinds and specially from land-based activities, which includes ML, as the sub-indicator 14.
1.
1b plastic debris density (UNEP, 2021).
This indicator is expected to be relevant for the Global Plastic Treaty.
Earth Observation data has been recommended as the primary source of data to derive SDG 14.
1.
1b sub-indicators (UNEP, 2021).
Remote sensing of aquatic litter is an emerging area of R&D that has been going through a rapid evolution over the last few years (GIZ, 2023; Maximenko et al.
, 2016).
The Copernicus Sentinel-3 mission, carrying the Ocean and Land Colour Imager (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) has been recommended by UNEP as a potential data source for relevant plastic patches data in international waters.
The capabilities of monitoring ML using Sentinel-3 are yet to be well documented, in contrast with efforts that have been done with the Copernicus Sentinel-2 Multi Spectral Instrument (MSI), which has received wide attention and demonstrated promising findings (Cózar & Arias et al.
, 2024; Lavender, 2022; Topouzelis et al.
, 2019).
However, Sentinel-2 has a limited geographic coverage, restricted to within 100 km from most coastlines covering part of member state exclusive economic zone (EEZ, ~370.
4 km), and a signal-to-noise ratio (SNR) not optimized for marine observations.
The scope of this study was to review and evaluate EO technologies that have the potential to address the data needs for deriving SDG 14.
1.
1b sub-indicators.
We conducted an extensive assessment that involved mission-level trade-off and Strengths, Weaknesses, Opportunities and Threats (SWOT) analyses.
The research objective was to explore the suitability of the currently available EO sensors for reporting on ML in international waters.
A candidate EO-based system is also recommended regarding a sensor or set of sensors that could yield the best level of descriptors.
The analysis also led to identifying promising processing techniques that may respond to the expectations of SDG 14.
1.
1b sub-indicators.
Exact capabilities and performance are expected to be assessed in future research.

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