Lake Turbidity Mapping Using an OWTs-bp Based Framework and Sentinel-2 Imagery

Abstract

Lake turbidity, representing a general indicator of water ‘cloudiness’, is a key parameter in many monitoring programs. It is not possible to cover all lakes with frequent in situ monitoring. Sentinel-2 MultiSpectral Imager (MSI) can help to fill the gaps if a robust turbidity retrieval methodology is developed. Previously published results demonstrated the usefulness of MSI at a limited regional scale, while our aim was to develop methodology that allows monitoring turbidity over the whole of China. We proposed methodology with a reflectance that can be classified into optical water types (OWTs), and then a back propagation neural network model (BP-TURB) is used to estimate turbidity. The reflectance of in situ lake samples extracted from MSI imagery was clustered as three OWTs, and validation performance was satisfactory: R2 > 0.81, RMSE < 17.54, and MAE < 11.20. This allowed us to map turbidity in all Chinese lakes, of which the area is larger than 1 km2. A larger percentage of clear lakes (53.26%) with low turbidity levels (<10 NTU) was found in 2020 than in 2015 (37.43%). Lakes in the plateau regions generally exhibited lower turbidity than those situated in the plains regions, for which the turbidity patterns were determined by lake volume, averaged depth, and elevation. We demonstrated that the Sentinel-2 MSI data with the novel approach proposed by us allows for mapping lake turbidity over a large variety of lakes and extensive geographic conditions, as well as for revealing temporal changes in these lakes and their links to lake abiotic characteristics.