Algal Biological Features Viewed in Satellite Observations: A Case Study of the Bohai Sea

Abstract

Algal cell abundance weakly depends on inherent optical properties and chlorophyll-a concentration in the Bohai Sea, so it is very hard to derive algal cell abundance (ACA) from ocean color data using a simple bio-optical model. To obtain ACA for biological communication at large scale, a neural network model has been developed and then applied for investigating the changing monthly trend of ACA, intracellular chlorophyll-a concentration, and cell size in the Bohai Sea using MODIS data from 2002 to 2015. The results showed that the neural network model could provide an accurate log-transformed value of algal cell abundance (LACA) from ocean color images whose retrieval uncertainty did not exceed 9%. Furthermore, when the model was applied to map the monthly mean LACA and then further convert it to cell size in the Bohai Sea, the results showed that the satellite-derived monthly mean cell size varied from 4.81 to 15.29 μm. The decreasing monthly mean algal cell abundance and increasing monthly mean chlorophyll-a concentration imply that the monthly mean intracellular chlorophyll-a concentration from 2002 to 2015 increased, which indicates that the waters in the Bohai Sea became more eutrophic over those 14 years. Moreover, due to seasonal variations in vertical mixing or other physical forcing factors, the ACA and cell size exhibited significant seasonal variations. Although further tests are required to validate the model’s robustness, these preliminary results indicate that the neural network model is an encouraging approach to exploiting more novel biological parameters such as the LACA from ocean color satellites for oceanic communication.