Remote Sensing Application for Monitoring Microplastic in Aquatic Environments

Plastic pollution within aquatic ecosystems has emerged as a severe anthropogenic issue adversely affecting aquatic environments and human health. The conventional methods of micro- and macroplastic detection (e.g., containment booms, nets, and trawls) have drawbacks involving relatively high cost, need for personnel, and low spatio-temporal coverage. Accurate and faster methods of plastic litter detection, quantity estimation, identification of sources, and distribution pattern are thereby essential to provide an effective plastic waste management system. Remote sensing and GIS provide an efficient solution for plastic litter detection with more spatio-temporal coverage. Limitations regarding continuous tracking of aquatic plastic waste using remote sensing necessitate the need for exploring different approaches involving the integration of machine learning algorithms. This review delves into various studies on the detection of micro- and macroplastics in aquatic environments and offers an overview of current approaches, challenges, detection techniques, and future prospects. The various approaches, including the use of satellite remote sensing, the synergetic approach involving the integration of satellite and UAV technologies, the utilization of radar and statistical modeling techniques, and the integration of machine learning algorithms with spaceborne remote sensing, are comprehensively discussed. The potentials and challenges of the different approaches and methodologies used in micro- and macroplastic detection are also analyzed. By highlighting areas for improvement and proposing potential avenues for research, it aims to contribute to the ongoing efforts in developing efficient and reliable remote sensing techniques for monitoring micro- and macroplastic pollution in aquatic ecosystems.