High precision water quality retrieval in Dianchi Lake using Gaofen 5 data and machine learning methods

Water quality indicators (WQI) reflect both the current state and the changing trends of water quality. Extracting these indicators from remote sensing data enables rapid and efficient inversion of water quality conditions, providing a key step in predicting water pollution. The low-precision inversion results of WQI limit the understanding of the ecological safety of water resources. In this paper, the Advanced Hyperspectral Imager (AHSI) data from the GF-5 satellite was used to analyze the Dianchi Lake. A spectrum processing method based on the Savitzky-Golay Standard Normal Variate transformation (SG-SNV) was developed, alongside optimal inversion techniques, to enhance the accuracy of predictions for 6 WQI in Dianchi, including CODcr, NH3-N, TP, TN, pH, and Chl. The results indicated that (1) For all inversion models, the determination coefficients (R²) exceeded 0.85. The Back Propagation Nondominated Sorting Genetic Algorithm-II (BP-NGA) model consistently yielded positive results for most WQI. (2) Water quality in Dianchi varied significantly by region and season. (3) It was recommended to build wetlands and ecological parks on the southwest side of Dianchi and improve sewage interception pipelines on the northeast side to lessen the risk of eutrophication by reducing the inflow of nitrogen and phosphorus.