A Remote Sensing Algorithm of Column-Integrated Algal Biomass Covering Algal Bloom Conditions in a Shallow Eutrophic Lake

Abstract

Column integrated algal biomass provides a robust indicator for eutrophication evaluation because it considers the vertical variability of phytoplankton. However, most remote sensing-based inversion algorithms of column algal biomass assume a homogenous distribution of phytoplankton within the water column. This study proposes a new remote sensing-based algorithm to estimate column integrated algal biomass incorporating different possible vertical profiles. The field sampling was based on five surveys in Lake Chaohu, a large eutrophic shallow lake in China. Field measurements revealed a significant variation in phytoplankton profiles in the water column during algal bloom conditions. The column integrated algal biomass retrieval algorithm developed in the present study is shown to effectively describe the vertical variation of algal biomass in shallow eutrophic water. The Baseline Normalized Difference Bloom Index (BNDBI) was adopted to estimate algal biomass integrated from the water surface to 40 cm. Then the relationship between 40 cm integrated algal biomass and the whole column algal biomass at various depths was built taking into consideration the hydrological and bathymetry data of each site. The algorithm was able to accurately estimate integrated algal biomass with R 2 = 0.89, RMSE = 45.94 and URMSE = 28.58%. High accuracy was observed in the temporal consistency of satellite images (with the maximum MAPE = 7.41%). Sensitivity analysis demonstrated that the estimated algal biomass integrated from the water surface to 40 cm has the greatest influence on the estimated column integrated algal biomass. This algorithm can be used to explore the long-term variation of algal biomass to improve long-term analysis and management of eutrophic lakes.Bahía Blanca Estuary in Argentina [23,24] and the Great Barrier Reef in Australia [25,26]. Inland eutrophication has led to increasing occurrences of cyanobacteria blooms, with impact on water, fisheries and biodiversity [27,28].Remote sensing has been used to assess eutrophication status with major success. In addition, it is less labor-intensive and time-consuming [29]. Traditional remote sensing provides information on the extent of cyanobacterial blooms [30][31][32][33], the cyanobacterial blooms intensity [30] and pigment concentrations including chlorophyll (Chl-a) [13,[34][35][36][37] and phycocyanin [38][39][40]. However, these estimations focus on near-surface investigations, which are not accurate because they do not consider the vertical migration of algae. In reality, some algae (i.e., cyanobacterial) are able to adjust their vertical position within the water column [29,41]. Temperature, light conditions, nutrient availability and interactions with other micro-organisms, grazers and pathogens also have an influence on algae's vertical migration [42][43][44]. Moreover, the vertical distribution can variate between seasons and algae species [45].The limited relationship between surface and total column algal biomass has been evidenced by rapid changes observe...

Retrieval of Chlorophyll-a Concentrations Using Sentinel-2 MSI Imagery in Lake Chagan Based on Assessments with Machine Learning Models

“…On the other hand, numerous algorithms have been proposed for estimating Chl-a, including empirical algorithms and analytical algorithms. Analytical algorithms focus on the mechanism of radiation in water, making the model physically explainable [18,19]. However, the cumbersome analysis of complex formulas requires a higher level of computation and model derivation.…”