Satellite sensors as an emerging technique for monitoring macro- and microplastics in aquatic ecosystems

Abstract

Plastic pollution in aquatic ecosystems has been identified as a growing global water pollution threat that is negatively impacting water quality and, as a result, affecting the health of humans, aquatic animals, and wildlife. Therefore, it presents a global environmental catastrophe that requires immediate attention. Plastics in water (in their different forms, macro-, meso-, micro-, and nanoplastics) are contaminants of emerging concerns that have since evolved to be a global environmental threat. Despite increasing levels of pollution in aquatic ecosystems, there are insufficient monitoring data to evaluate the extent of the catastrophe. Traditional methods of monitoring plastics in water are constrained by high sampling costs, intensive labor, and limited temporal and spatial coverage, which results in limited monitoring data. Thus, insufficient monitoring data limit our understanding of the true quantities and persistence of plastic particles in aquatic ecosystems, as well as the extent to which they impact the aquatic environment. There is increasing availability of free big geospatial data (amounting to petabytes/day) from satellite sensors for potentially monitoring plastics. This provides a possible solution to these challenges by minimizing the fieldwork required and therefore reducing the costs and sampling time. The study purpose of this review is to analyze advances in emerging technology such as the use of satellite sensors to monitor the occurrence of macro- and microplastics in freshwater, ultimately aimed at creating new operational monitoring solutions. This review: (1) examines the literature to identify trends, accomplishments, and limitations of using satellite data to monitor plastics in water; (2) identifies and compares traditional, and machine and deep learning satellite image classification methods for monitoring plastics in water; and (3) identifies research gaps and summarizes future perspectives and recommendations to improve monitoring methods.

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

“…Mukonza and Chiang [19] conducted another review highlighting the potential of machine and deep learning classifiers, based on high-resolution optical sensors and syntheticaperture radar (SAR) satellites, for monitoring micro-and macroplastics in aquatic ecosystems. Specifically, SAR satellites can detect changes in backscatter signal, which is a function of surface roughness in water bodies, primarily caused by wind-driven waves.…”

Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy

“…Plastic pollution in aquatic ecosystems is an emerging environmental issue worldwide [1]. Rivers transport plastics (1.2-2.41 million tons/year) accounting for 80% of ocean plastics, and it is expected to reach 2.75 million tons by 2040 [2].…”

“…It is partly attributed to the fact that most MP studies have primarily relied on in situ measurements, which were limited in space and time. Hence, their sampling schemes were often insufficient for tracking the highly dynamic MP transport [1]. On the other hand, other studies developed numerical models, e.g., CaMPSim-3D [5] and INCA [6] to address the limitations of in situ measurements through providing highly-detailed spatiotemporal data.…”

“…Therefore, the main aim of the present study is to address this research gap by developing a neural network-based model to estimate MPC in rivers based on varying optical [Sentinel-2 (S-2) and PlanetScope (PS)] and active [Sentinel-1 (S-1)] satellite sensors. The goals of the study are: (1) to evaluate the direct correlation between SSC/MPC and reflectance/backscattering of the designated sensors, (2) to test the efficiency of SSC (measured and estimated by satellites) as a proxy for estimating MPC considering both overall and individual hydrological conditions, (3) to develop neural network-based models to estimate SSC/MPC by various sensors and compare their estimation accuracy, and finally (4) to identify the most important bands for SSC/MPC estimation for each sensor.…”

On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring

“…Hence, the primary aim of this paper is to enhance the existing body of knowledge by not only summarising key academic findings in the field of monitoring MD&SP but also engaging in a thorough analysis of significant challenges. Noticeably, this primary aim differentiates this review from other relevant surveys (Salgado-Hernanz et al, 2021;Topouzelis et al, 2021;Gnann et al, 2022;Mukonza and Chiang, 2022;Veettil et al, 2022;Vighi et al, 2022;Politikos et al, 2023) as it provides a more comprehensive discussion on ongoing challenges and future research directions. The paper concludes by outlining several crucial directions for potential research as envisioned by the author, intended to provide a guiding framework for future studies.…”