Remote Sensing-Based Inversion of Suspended Solids Concentration in Karst Plateau Lakes Utilizing Extremely Randomized Trees

Remote sensing inversion of suspended solids (SS) in karst plateau lakes is challenging due to spectral mixing and strong nonlinearity. This study develops an inversion framework using Sentinel-2 MSI imagery and field data from 40 sampling sites at Pingzhai Reservoir. An Extremely Randomized Trees (ERT) model was constructed with 20 spectral features selected via correlation analysis and multicollinearity control. The ERT model demonstrated excellent performance (R2 = 0.90, RMSE = 0.95 mg/L, MAE = 0.66 mg/L), outperforming conventional linear models (improved by 57%) and other machine learning algorithms. The near-surface SS concentrations exhibited significant seasonality, peaking in spring and summer (7.3 mg/L) and concentrating in inflow zones. TOC and water temperature were identified as key drivers, while a negative correlation with total nitrogen under alkaline conditions highlighted unique karst nutrient dynamics. This framework provides an accurate, high-spatiotemporal-resolution remote sensing solution for SS monitoring, supporting water-quality management in karst regions.