Water quality parameters retrieval and nutrient status evaluation based on machine learning methods and Sentinel- 2 imagery: a case study of the Hongjiannao Lake

Abstract

A timely and accurate understanding of lake water quality is significant for maintaining ecological balance, ensuring water resource security, and promoting regional sustainable development. However, due to the varying numerical ranges and characteristics of different water quality parameters (WQPs), the selection of optimal retrieval algorithms is also different, which undoubtedly increases the complexity of different WQPs retrieval. To solve this problem, this study took the Hongjianao Lake in China as the research object, based on the measured data of chlorophyll-a (Chl-a), turbidity (TU), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH₃-N), electrical conductivity (EC) and potential of hydrogen (pH) and Sentinel- 2 images, compared the ability of Boruta, recursive feature elimination (RFE) and shapley additive explanations (SHAP) methods to obtain the optimal feature subset. The random forest algorithm (RF), back propagation neural network algorithm (BP), and support vector machine algorithm (SVM) algorithms were used to retrieve lake water quality, and the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were used to evaluate the prediction accuracy of multiple combined models from different aspects. The SHAP method was employed to quantify the contribution of input characteristics to WQPs. Subsequently, an integrated nutrient state index was established by utilizing the inversion results of Chl-a, COD, TN, TP, and NH₃-N, along with the entropy weight method to assess the nutrient status level. The results showed that the optimal model, SHAP-RF, has better retrieval accuracy for WQPs (Chl-a, R² = 0.66, RMSE = 0.28 µg/L; COD, R² = 0.73, RMSE = 7.30 mg/L; EC, R² = 0.69, RMSE = 160.58 us/cm; NH₃-N, R² = 0.59, RMSE = 0.11 mg/L; pH, R² = 0.73, RMSE = 0.007; TN, R² = 0.84, RMSE = 1.09 mg/L; TP, R² = 0.65, RMSE = 0.015 mg/L; TU, R² = 0.63 RMSE = 3.17 ntu). The most sensitive spectral bands for Chl-a and NH₃-N were the combination of green and red-edge bands. The sum of blue and near-infrared (NIR) bands was the most important in the inversion of COD. The product of the red and NIR bands played a crucial role in pH inversion. The subtraction between the green and red bands was the first choice for EC inversion. The red-edge bands and their combination contribute significantly to TN inversion. TP was most sensitive to the red-edge bands and shortwave infrared bands. The red band exhibited the highest sensitivity to TU inversion. The primary pollutants in Hongjiannao Lake were TN, TP, and COD. The water quality had deteriorated, with 29% of the water exhibiting light nutrient status, 53% displaying middle nutrient status, and 18% enduring hyper nutrient status. The results were highly significant for precisely assessing the water quality and nutrient levels in lakes.