  The increasing level of marine plastic pollution poses severe threats to the
marine ecosystem and biodiversity. The present study attempted to explore the
full functionality of open Sentinel satellite data and ML models for detecting
and classifying floating plastic debris in Mytilene (Greece), Limassol
(Cyprus), Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support
Vector Machine (SVM) and Random Forest (RF) were utilized to carry out the
classification analysis. In-situ plastic location data was collected from the
control experiment conducted in Mytilene, Greece and Limassol, Cyprus, and the
same was considered for training the models. Both remote sensing bands and
spectral indices were used for developing the ML models. A spectral signature
profile for plastic was created for discriminating the floating plastic from
other marine debris. A newly developed index, kernel Normalized Difference
Vegetation Index (kNDVI), was incorporated into the modelling to examine its
contribution to model performances. Both SVM and RF were performed well in five
models and test case combinations. Among the two ML models, the highest
performance was measured for the RF. The inclusion of kNDVI was found effective
and increased the model performances, reflected by high balanced accuracy
measured for model 2 (~80% to ~98 % for SVM and ~87% to ~97 % for RF). Using
the best-performed model, an automated floating plastic detection system was
developed and tested in Calabria and Beirut. For both sites, the trained model
had detected the floating plastic with ~99% accuracy. Among the six predictors,
the FDI was found the most important variable for detecting marine floating
plastic. These findings collectively suggest that high-resolution remote
sensing imagery and the automated ML models can be an effective alternative for
the cost-effective detection of marine floating plastic.
