Ocean Color Net (OCN) for the Barents Sea

Over recent years, rapid environmental changes in the Arctic and subarctic regions have caused significant alterations in the ecosystem structure and seasonality, including the primary productivity of the Barents Sea. This work aims at improving methodology for studying these features, by estimating chlorophyll-a (chl-a) concentrations in the transitional Barents Sea by remotely sensing its optical properties, in order to better understand the large-scale algal bloom dynamics in the region. The in-situ measurements of chl-a are collected from the year 2016 to 2018 over a wide area of the Barents Sea to cover the spatial and temporal variations in chl-a concentration. Optical images of the Barents Sea are captured by the Multi-Spectral Imager Instrument on Sentinel-2. Using these remotely sensed optical images and the in-situ measurements, we propose a match-up dataset creation method based on the distribution of the remotely sensed reflectance spectra. Different Machine Learning (ML) techniques are assessed to estimate concentration of chl-a using the match-up dataset. Most of these techniques have not been investigated before in the subarctic region such as the Barents Sea. The Ocean Color Net (OCN) regression model proposed in this study has outperformed other ML-based techniques including Support Vector Regression, Gaussian Process Regression, and the globally trained Case-2 Regional/Coast Colour (C2RCC) processing chain model C2RCC-Nets, as well as empirical methods based on spectral band ratios. A wide range of experiments has demonstrated the effectiveness of the proposed OCN for ocean color remote sensing in the subarctic region. The performance of the OCN is also presented spatially by computing chl-a maps in the Barents Sea.