Abstract: Although remote sensing has become a common tool for monitoring mountainous reservoirs, studies on the detection of phytoplankton community compositions (PCCs) remain insufficient. Based on satellite and field data, we developed a mathematical model incorporating fuzzy logic and probabilistic methods to directly estimate the biomass of seven different phytoplankton species in Huating Lake. Water surface temperature (WST) and chlorophyll-a concentration ([Chl-a]) were selected as input parameters for this model. The WST data were processed using a single-channel algorithm that combined the brightness temperature conversion model and land surface emissivity algorithm. Inversion of [Chl-a] was conducted using an empirical approach to compare the four models developed for the two sensitive reflectance bands. The [Chl-a] values obtained from these models were significantly correlated with the field data (R > 0.8). The optimal model was selected based on validation results. After obtaining the inversion results for the WST and [Chl-a], we applied a fuzzy probabilistic model to determine the PCCs in Huating Lake from 2013 to 2023. A comparison with the measured data confirmed that this method reliably estimated PCC biomass (R > 0.65). However, the modeling accuracy was not particularly high for Bacillariophyta and Euglenophyta with high biomass. We analyzed the spatial and temporal distribution of PCCs in Huating Lake over 10 years from 2013 to 2023 and found that the results were reasonable. The results demonstrate that the fuzzy probabilistic approach offers a novel methodology for estimating the biomass of seven phytoplankton species. This method facilitates the expansion of remote-sensing technology for monitoring PCC changes in mountainous reservoirs and provides scientific data support for understanding algal bloom mechanisms and developing prevention strategies.
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