关键词: 骆马湖, Chl-a浓度, 遥感反演, 机器学习, 萤火虫算法

Abstract: This study constructs a water quality inversion model based on mathematical statistics and machine learning methods by combining water quality parameters, spectral data collected on June 15, 2023, and synchronous Sentinel-2 imagery data to quantitatively invert the Chl-a concentration in Luoma Lake. Through comparative analysis, the model with the best performance is selected to analyze the Chl-a status in Luoma Lake. The study found that Chl-a shows a high correlation with the B5 and B9 bands of the Sentinel-2 imagery, and this correlation is further enhanced after band combination processing. In the Chl-a inversion model, the FA-SVR model demonstrates the highest accuracy (R²=0.86, RMSE=2.77, MAE=2.10) compared to traditional mathematical statistical regression models and other machine learning models (FA-RF, FA-XGBoost). The inversion results reveale that the nearshore area in the northeastern part of Luoma Lake has higher Chl-a concentrations, which may be related to the presence of fishpond farming and high eutrophication in the northern waters. This study highlights the significant application value of machine learning technology in improving the accuracy of water quality remote sensing inversion, provides important technical support for water quality monitoring and management in Luoma Lake.

Key words: Luoma Lake, Chlorophyll-a concentration, remote sensing inversion, machine learning, firefly algorithm