Monitoring of lake water quality along with trophic gradient using landsat data

Effect of differential trophic states on remote sensing-based monitoring and quantification of surface water quality is an important but understudied context. Landsat ETM+ data-based multiple linear regression models were conducted to quantify dynamics of lake surface water quality along oligotrophic-to-eutrophic gradient and to explore the influence of trophic state on the detection of water quality dynamics by the best multiple linear regression models. The best multiple linear regression models of dissolved oxygen, chlorophyll-a, Secchi depth, water temperature, and turbidity had R-adj(2) values ranging from 36.2 % in water temperature to 93.1% in dissolved oxygen for eutrophic Yenicaga Lake and from 36.1 % in Secchi depth to 99.7 % in water temperature for oligotrophic Abant Lake. The difference in the trophic state between Lakes Abant and Yenicaga, significantly affected the composition of the nine Landsat ETM+ spectral bands included in the multiple linear regression models as well as the predictive power of the multiple linear regression models. Remote sensing-based monitoring of lake water quality variables appears to be promising in terms of devising adaptive management decisions towards sustainability of water resources.