|Pyo, Jongcheol - University Of Ulsan College Of Medicine|
|Haseong - University Of Ulsan College Of Medicine|
|Lee, Hyuk - National Institute Of Environmental Research|
|Nam, Gibeon - National Institute Of Environmental Research|
|Im, Jungho - University Of Ulsan College Of Medicine|
|Cho, Kyung - University Of Ulsan College Of Medicine|
Submitted to: Remote Sensing Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/11/2016
Publication Date: 2/18/2016
Citation: Pyo, J., Haseong, Pachepsky, Y.A., Lee, H., Nam, G., Kim, M.S., Im, J., Cho, K.H. 2016. Chlorophyll-a concentration estimation with three bio-optical algorithms: correction for the low concentration range for the Yiam Reservoir, Korea. Remote Sensing Letters. 7(5):407–416.
Interpretive Summary: Measurement of radiation adsorption offers an efficient method to estimate concentrations of various substances in surface waters. Concentration of chlorophyll a is used to evaluate the level of algal bloom that affects microbial quality of irrigation water. During the measurement campaign at the Yiam reservoir, we observed that existing methods to estimate chlorophyll a concentrations give erroneous results if concentrations of chlorophyll a are small. We proposed the efficient correction method that decreased errors by almost two times. Results of this work will be useful in monitoring algal blooms and microbial contamination in that they offer better use of optical methods to diagnose and monitor pollution in surface waters.
Technical Abstract: Bio-optical algorithms have been applied to monitor water quality in surface water systems. Empirical algorithms, such as Ritchie (2008), Gons (2008), and Gilerson (2010), have been applied to estimate the chlorophyll-a (chl-a) concentrations. However, the performance of each algorithm severely degrades at concentrations notably lower than 10 mg/m3. This could be attributed to the chl-a-specific absorption coefficient that became less consistent at low chl-a concentrations. Nonetheless, no effort has been made in previous studies to correct existing algorithms. In this study, we propose a correction approach to improve their performance for chl-a estimation in Yiam reservoir, Korea. Estimated chl-a concentrations improved after applying the correction process proposed in this study; NSE values increased from 53% to 65% and RMSE decreased from 39% to 43%, respectively. Further research is needed to verify the correction approaches for different years or study sites.