Analyzing spatiotemporal variation in suspended particulate matter in lakes using remote sensing

【Objective】Suspended particulate matter (SPM) in lakes not only affects water quality but also influences ecological functions. This paper investigates the feasibility of using remote sensing to analyze the spatiotemporal variation in SPM in lakes, with an application to Daihai Lake.【Method】Field measurements were conducted to collect spectral and SPM concentration data in Daihai Lake. The accuracy of five machine learning inversion models - backpropagation (BP) neural network, convolutional neural network (CNN), random forest (RF), support vector machine (SVM), and radial basis function (RBF) neural network-was compared with that of traditional empirical models using Sentinel-2 MSI satellite data. The most accurate model was selected to estimate SPM concentrations across the lake.【Result】① Machine learning models outperformed traditional empirical models. Among them, the BP neural network achieved the highest accuracy, with a three-fold cross-validation test using synchronous ground data yielding R2=0.76, RMSE=4.66 mg/L, and MAPE=26.57%. ② All nine input variables in the BP model contributed positively to model performance. The most influential variable was the B4·B5, followed by B4, B4+B5, and B5·(B4+B5). ③ From 2017 to 2023, annual average SPM concentrations ranged from 8.43 to 11.68 mg/L, showing a slight downward trend. Monthly averages SPM concentration from May to October ranged from 7.77 to 10.84 mg/L, with May exhibiting the highest concentrations. After June, SPM levels generally declined and stabilized. Spatially, the highest concentrations occurred near the shore, particularly in the southwestern and eastern parts of the lake. ④ SPM concentrations were significantly and positively correlated with wind speed (r = 0.61, p＜ 0.001), while correlations with air temperature and precipitation were not significant.【Conclusion】Remote sensing combined with machine learning provides an effective and accurate method for monitoring SPM in lakes. This approach can help to improve management and ecological protection of lake environments. Among all models evaluated, the BP neural network is most accurate and stable, followed by the CNN model.