Observing the ocean provides us with essential information necessary to study and understand marine ecosystem dynamics, its evolution and the impact of human activities. However, observations are sparse, limited in time and space coverage, and unevenly collected among variables. Our work aims to develop an improved deep-learning technique for predicting relationships between high-frequency and low-frequency sampled variables. Specifically, we use a larger dataset, EMODnet, and train our model for predicting nutrient concentrations and carbonate system variables (low-frequency sampled variables) starting from information such as sampling time and geolocation, temperature, salinity and oxygen (high-frequency sampled variables). Novel elements of our application include (i) the calculation of a confidence interval for prediction based on deep ensembles of neural networks, and (ii) a two-step analysis for the quality check of the input data. The proposed method proves capable of predicting the desired variables with relatively small errors, outperforming the results obtained by the current state-of-the-art models.
