Satellite remote sensing of turbidity in Lake Xingkai using eight years of OLCI observations

In the context of global climate change and land use change, both of which significantly affect lake ecosystems, rainfall and wind conditions play a crucial role in lake mixing processes. Furthermore, land use changes impact water quality through modifications in runoff and sediment inputs. These factors exert a profound influence on lake ecosystems, thereby necessitating further investigation into their extent and ultimate consequences. In this study, we developed a band ratio model for estimating the turbidity concentration of Lake Xingkai (XKH), a border lake between China and Russia, using data from the Sentinel-3 Ocean and Land Color Instrument (OLCI). The model demonstrated a high degree of accuracy, with an R² of 0.84, a root mean square error (RMSE) of 27.08 NTU, a mean absolute error (MAE) of 16.58 NTU, and a mean absolute percentage error (MAPE) of 21.44% within the turbidity range of 20-400 NTU. The model was applied to 1240 cloud-cleared OLCI images from 2016 to 2023. The following findings were identified: (1) The optimal band for turbidity estimation was identified, and a robust model was developed based on the spectral response of turbidity in XKH; (2) monthly and annual analyses revealed a distinct upward trend in turbidity from July to October in the sub-region of XKH influenced by the Muling River tributary, differing from other areas of the lake. (3) By integrating meteorological and land use data, we investigated the influence of land use change on turbidity, uncovering the formation of persistent, distinctive river plume during periods of minimal climate impact. (4) Subsequent analysis revealed a correlation between turbidity and the occurrence of algal blooms. Therefore, monitoring turbidity changes can serve as an early warning for algal bloom events, offering valuable insights into the combined effects of climate and environmental changes on lake ecosystems.