Checking Landsat 8 OLI’s predictive power in the retrieval of chlorophyll-a and phycocyanin concentrations of a reservoir with high frequency field data

Abstract

Increased population, global warming, climate change, environmental pollution, agriculture, and many other issues make the monitoring of water bodies more and more critical with each day. Among the water quality variables to monitor, chlorophyll-a and phycocyanin are very crucial, as the former is strongly related to the phytoplankton dynamics, and the latter is an indicator of blue-green algae or cyanobacteria. As field trips are tiresome and difficult, satellite remote sensing methods have been developed continuously, yet most of the time their validation was insufficient since the aforementioned water quality variables may change dramatically with time. Hence, this study checked many commonly used algorithms reported to work well for chlorophyll-a retrieval with Landsat 8 OLI and an autosampler data which measures chlorophyll-a and phycocyanin in every 10 minutes. If not for the chlorophyll-a yet, a few band ratio algorithms and B1 and B6 of Landsat 8 OLI produced really promising prediction accuracies.