Plastics are globally dispersed and reported at a growing rate and at increasing concentrations in marine ecosystems. Due to their persistence in aquatic environments the global plastic problem will last for decennia. In this context, removal of plastic at the source prior to reaching the marine environment is instrumental. Hence, plastic detection methods and plastic remediation measures are urgently needed and may become obligatory in the near future. A first prerequisite to take effective plastic remediation measures is to know where and when action should be taken. However, to date there is a critical knowledge gap about the whereabouts of plastics and about their flux towards the marine environment. This information is crucial to fast track cost-efficient plastic remediation measures. A central objective in this project is to develop a two-dimensional-horizontal (2DH) plastic dispersal model. The model will be calibrated and validated with experiments and field sampling data. In this context, plastics will be identified from remote sensing reflectance data through image recognition algorithms (‘Machine Learning’), hence resulting in an automated plastic detection method. This information in combination with in situ sampling will validate the 2DH-model.