Monitoring the Vertical Variations in Chlorophyll-a Concentration in Lake Chaohu Using the Geostationary Ocean Color Imager

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 14; p. 2611
• Main Authors: Li, Hanhan, Wei, Xiaoqi, Huang, Zehui, Liu, Haoze, Ma, Ronghua, Wang, Menghua, Hu, Minqi, Jiang, Lide, Xue, Kun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Algae; Algal blooms; Algorithms; Aquatic ecosystems; Atmospheric correction; Chlorophyll; chlorophyll-a concentration; Cyanobacteria; Decision trees; Diurnal variations; Environmental monitoring; Eutrophic environments; Eutrophic lakes; Eutrophication; geostationary satellite; GOCI; Image processing; Lake management; Lakes; Ocean color; Parameter estimation; Phytoplankton; Plankton; Regression analysis; Regression models; Remote monitoring; Remote sensing; Spatial variations; Vertical distribution; Water depth; Water quality; Wind speed; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16142611

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field Rrs (Rrs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI Rrs data (Rrs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R2 = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAIRrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAIRrs (R2 = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with Rrs_G and environmental parameters (R2 = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management.