Improving the accuracy of estimation of eutrophication state index using a remote sensing data-driven method: A case study of Chaohu Lake, China

Trophic Level Index (TLI) is o en used to assess the general eutrophication state of inland lakes in water science, technology, and engineering. In this paper, a data-driven inland-lake eutrophication assessment method was proposed by using an arti cial neural network (ANN) to build relationships from remote sensing data and in-situ TLI sampling. In order to train the net, Moderate Resolution Imaging Spectroradiometer (MODIS, which has a revisit cycle of 4 times per day) data were combined with in-situ observations. Results demonstrate that the TLI obtained directly from remote-sensing images using the data-driven method is more accurate than the TLI calculated from the water quality factors retrieved from remote-sensing images using a multivariate regression method. Spatially continuous and quasi-real time results were retrieved by using MODIS data. is method provides an e cient way to map the TLI spatial distribution in inland lakes, and provides a scheme for increased automation in TLI estimation.