Monitoring and modelling of chlorophyll-a concentrations in rivers using a high-resolution satellite image: a case study in the Nakdong river, Korea

Abstract

The feasibility of using remote-sensing data with high spatial resolution was assessed for monitoring and modelling of chlorophyll-a (chl-a) in river waters. Two-band and three-band reflectance models including the red-edge band were examined as spectral coefficients using a RapidEye image for river waters, where the scale is smaller and narrower than for ocean waters. A red‒red-edge‒NIR three-band model calculated by a cubic function explained 73% of variance in the estimated data using the relationship between spectral indices such as absorption coefficients obtained using the model and chl-a concentrations and performed better than the red‒red-edge two-band. Chl-a concentrations were simulated by a one-dimensional water quality model, QUALKO2, and image-derived and measured chl-a concentrations were applied in the calibration step of simulation. The image-derived chl-a dataset showed more stable calibration throughout the study area and enhanced the results rather than measured data. It is expected that chl-a estimation techniques using high resolution satellite data, RapidEye, have the capability to support rapid and widespread water quality monitoring and modelling, when a field dataset is not large or precise enough to do it, but still requires the improvement of estimation accuracy.