Abstract
The proliferation of algal blooms can lead to environmental issues. The phytoplankton responsible for these blooms are diverse. Different species of bloom-forming algae have distinct characteristics and hazards, and therefore need different treatment methods. An accurate and quick determination of the spatial and temporal distribution characteristics of different algal species is crucial for lake ecological restoration. Based on the differences in remote sensing reflectance (Rrs) of various typical algae species in eutrophic lakes (including Microcystis aeruginosa, Aphanizomenon sp., and Pseudanabaena sp. in Cyanobacteria and Chlorella sp. and Scenedesmus quadricauda in Chlorophytes), difference index and algae distinguishing index were developed to differentiate algae species. A validation, using an independent dataset from an indoor experiment and in-situ-measured and satellite-image-derived Rrs, showed that the algorithm can provide reliable results (overall accuracies of 81.97%, 81.25%, and 60.42%, respectively). According to Ocean and Land Color Instrument images of Lake Taihu in the period of 2016 to 2020, Microcystis was the dominant algae, followed by Pseudanabaena and Aphanizomenon. The dominance of the two types of Chlorophytes was less pronounced. The proportion of Microcystis as the dominant algae was highest in summer, while the proportion of Pseudanabaena peaked in winter. The proportion of Aphanizomenon varied slightly throughout the year, while the proportion of the two Chlorophytes peaked in winter. In terms of spatial distribution, the patterns in spring and autumn were relatively similar. In summer, approximately 80% of the lake was dominated by Microcystis. In winter, Chlorella and Scenedesmus were more prevalent along the southeastern shore of Lake Taihu. The construction and application of this model can provide a technical support for prediction and prevention of blooms in inland lakes.
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