Coupling remote sensing with computational fluid dynamics modelling to estimate lake chlorophyll- a concentration

Abstract

A remotely sensed image of Loch Leven, a shallow meso-eutrophic lake in Central East Scotland, UK, revealed a strong gradient in chlorophyll- a (chl- a) concentration. As a means of interpreting the spatial distribution of chl- a in this image, a combined three-dimensional computational fluid dynamics (CFD) and ecological model was run using estimates of the environmental and planktonic conditions concurrent with and preceding the time of image acquisition. The post facto modelling of the dynamics of the lake produced spatial distributions of surface chl- a that were consistent with that evident in the remotely sensed image. It is proposed that CFD modelling benefits the interpretation of remotely sensed images of water bodies in that it may be used to infer the causes of the spatial distributions evident in the remotely sensed imagery. This is because modelling extends the analysis into the temporal and vertical domains. However, the value of combining CFD with remote sensing is limited by the quality and quantity of data available through surface observation and remote sensing, and the implications of this to the integration of CFD with remote sensing are discussed.