Abstract
Eutrophication has become a major environmental concern in Lake Taihu, with total phosphorus (TP) and total nitrogen (TN) serving as key indicative parameters. However, existing retrievals of TP and TN in Lake Taihu mostly rely on conventional empirical algorithms over limited periods, and the month-to-month spatial patterns of the two nutrients across the whole lake remain poorly characterized. To address this gap, the present study introduces Maximal Information Coefficient (MIC)-based feature selection into machine learning retrieval of TP and TN from Sentinel-2 imagery and produces month-to-month lake-wide maps over nine months. This study utilized Sentinel-2 satellite imagery and water quality data obtained from 15 automatic monitoring stations in Lake Taihu between January 2024 and December 2024 to develop retrieval models for TP and TN. Feature selection was first performed using the MIC. Subsequently, four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Backpropagation Neural Network (BP), and eXtreme Gradient Boosting (XGBoost)—were employed to establish TP and TN retrieval models. The final models were then selected to map the spatial distributions of the two nutrients. Results showed that the SVM model incorporating band combinations achieved the best TP retrieval performance (R2 = 0.65, RMSE = 0.03 mg/L), while RF yielded the highest accuracy for TN estimation (R2 = 0.46, RMSE = 0.36 mg/L), explaining only about 46% of the variance and indicating limited predictive power for TN retrieval. Temporally, TP concentrations across Lake Taihu remained relatively low from December to May, increased gradually from August to October, and reached the annual peak in October. Spatially, western lake zones exhibited higher TP concentrations than the eastern area. TN shared identical spatial distribution patterns with TP across the lake; temporally, TN concentrations reached the annual maximum in March and the annual minimum in August. These findings demonstrate the feasibility of satellite-based TP and TN retrieval for lake water quality monitoring and provide scientific support for water environment management in Lake Taihu.