Retrieval of Water Quality Parameters in Dianshan Lake Based on Sentinel-2 MSI Imagery and Machine Learning: Algorithm Evaluation and Spatiotemporal Change Research

Abstract

According to current research, machine learning algorithms have been proven to be effective in detecting both optical and non-optical parameters of water quality. The use of satellite remote sensing is a valuable method for monitoring long-term changes in the quality of lake water. In this study, Sentinel-2 MSI images and in situ data from the Dianshan Lake area from 2017 to 2023 were used. Four machine learning methods were tested, and optimal detection models were determined for each water quality parameter. It was ultimately determined that these models could be applied to long-term images to analyze the spatiotemporal variations and distribution patterns of water quality in Dianshan Lake. Based on the research findings, integrated learning algorithms, especially CatBoost, have achieved good results in the retrieval of all water quality parameters. Spatiotemporal analysis reveals that the overall distribution of water quality parameters is uneven, with significant spatial variations. Permanganate index (CODMn), Total Nitrogen (TN), and Total Phosphorus (TP) show relatively small interannual differences, generally exhibiting a decreasing trend in concentrations. In contrast, chlorophyll-a (Chl-a), dissolved oxygen (DO), and Secchi Disk Depth (SDD) exhibit significant interannual and inter-year differences. Chl-a reached its peak in 2020, followed by a decrease, while DO and SDD showed the opposite trend. Further analysis indicated that the distribution of water quality parameters is significantly influenced by climatic factors and human activities such as agricultural expansion. Overall, there has been an improvement in the water quality of Dianshan Lake. The study demonstrates the feasibility of accurately monitoring water quality even without measured spectral data, using machine learning methods and satellite reflectance data. The research results presented in this paper can provide new insights into water quality monitoring and water resource management in Dianshan Lake.