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

• DOI: 10.1016/j.jag.2024.103800
• Publication Date: 2024-04-01T03:11:40Z
• Authors: Honglei Guo; Wenyu Liu; Heng Lyu; Huaiqing Liu; Jiafeng Xu; Yunmei Li; Xianzhang Dong; Yuxin Zhu; Yiling Zheng; Song Miao
• Keywords: Oscillatoria; Secchi disk

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