Estimation of chromophoric dissolved organic matter and its controlling factors in Beaufort Sea using mixture density network and Sentinel-3 data

With the warming of the high-latitude regional climate, melting of permafrost, and acceleration of hydrological cycles, the Arctic Ocean (AO) has undergone a series of rapid changes in the past decades. As a dominant optical component of the AO, the variations in chromophoric dissolved organic matter (CDOM) concentration affect the physiological state marine organisms. In this study, machine learning retrieval model based on in situ data and mixture density network (MDN) was developed. Compared to other models, MDN model performed better on test data (R2 = 0.83, and root mean squared error = 0.22 m−1) and was applied to Sentinel-3 OLCI data. Afterward, the spatiotemporal distribution of CDOM during the ice-free (June–September) from 2016 to 2020 in the Beaufort Sea was obtained. CDOM concentration generally exhibited an upward trend. The maximum monthly average CDOM concentration appeared in June and gradually decreased thereafter, reaching its lowest value in September of each year. The maximum value appeared in June 2020 (0.91 m−1), and the minimum value was observed in September 2017 (0.81 m−1). The CDOM concentration nearshore was higher than that in other areas; and gradually decreased from offshore to the open sea. CDOM was highly correlated with salinity (R2 = 0.49) and discharge (R2 = 0.53), and the tight correlation between salinity and CDOM further suggested that terrestrial inputs were the main source of CDOM in the Beaufort Sea. However, sea level pressure contributed to the spatial variations in CDOM. When southerly wind prevailed and wind direction was aligned with the CDOM diffusion direction, the wind accelerated the diffusion of CDOM into the open sea. Meanwhile, seawater was diluted by the sea ice melting, resulting in decrease in CDOM concentration. Herein, this paper proposed a robust and near real-time method for CDOM monitoring and influence factor analysis, which would promote the understanding of AO CDOM budgets.