A Generalized Chlorophyll-A Estimation Model for Complexity-Diverse Arctic Waters

In this paper, we evaluate the possibility of using a machine learning Gaussian Process Regression (GPR) approach to monitor Chlorophyll-a content in Arctic waters by using the Sentinel 3 Ocean and Land Color Instrument. We develop the GPR model on a synthetic dataset, which represents both open ocean and coastal Arctic waters. This allows the model to be exposed to and trained on data from both kinds of aquatic environments. The chosen GPR model has previously been trained and tested in a different aquatic environment, representing a variety of complexity conditions, where it was demonstrated to have strong generalization capabilities. Our results suggest that this model can also be used for diverse Arctic water conditions as well.