Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm

Abstract Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-a concentrations. In this study, a total of 273 samples were collected from 45 typical lakes across China during 2017-2019. Here, we proposed applicable machine learning algorithms (i.e., linear regression model (LR), support vector machine model (SVM) and Catboost model (CB)), which integrate a broad scale dataset of lake biogeochemical characteristics using Multispectral Imager (MSI) product to seamlessly retrieve the Chl-a concentration. A K-means clustering approach was used to cluster the 273 normalized water leaving reflectance spectra [Rrs (λ)] extracted from MSI imagery with Case 2 Regional Coast Colour (CR2CC) processor into three groups. The pH, electrical conductivity (EC), total suspended matter (TSM) and dissolved organic carbon (DOC) from three clustering groups had significant differences (p