A novel algorithm for estimating phytoplankton algal density in inland eutrophic lakes based on Sentinel-3 OLCI images

Abstract

As one of the optically active components, phytoplankton are common photosynthetic organisms in oceans, nearshore, and inland water bodies. The variations in phytoplankton algal density play a crucial role in understanding primary productivity, carbon cycling, and early warning of algal blooms. In this study, three typical eutrophic lakes in China, Lake Taihu, Lake Chaohu, and Lake Dianchi, were taken as the research area. Algorithms for estimating algal density of cyanobacteria-dominated and non-cyanobacteria-dominated water types were developed based on Mie theory. The results demonstrated that the developed algorithm had favorable estimation performance for inland eutrophic lakes, with a determination coefficient (R2) of 0.88, a mean absolute percentage error (MAPE) of 51%, an unbiased mean absolute percentage error (UMAPE) of 39%, and a root mean square error (RMSE) of 23.99 × 106 cells/L. Furthermore, comparison with other algorithms for estimating algal density showed that the developed algorithm had the lowest MAPE of 60% and UMAPE of 43%, with the RMSE of 23.42 × 106cells/L. Extensive evaluation based on satellite-ground synchronous data demonstrated the applicability of the developed algorithm to the Sentinel-3 OLCI sensor, enabling the determination of spatial and temporal distribution characteristics of algal density in the three lakes from 2016 to 2022 using Sentinel-3 OLCI images. The results of algal density inversion revealed a continuous decreasing trend in algal density in Lake Dianchi from 2016 to 2022, while the algal density in Lake Taihu and Lake Chaohu both decreased after 2019.

Keywords

• Inland eutrophic lakes
• Algal density
• Quantitative estimation
• Mie theory
• Sentinel-3 OLCI