Shahvaran, A. R., Pour, H. K., Van Cappellen, P. (2022) Evaluation of Atmospheric Correction Processors and Semi-Empirical Algorithms for Retrieval of Chlorophyll-a Concentrations from Multi-Spectral Satellite Imagery over Western Lake Ontario. In Fall Meeting 2022. AGU. https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1152585

Eutrophication of lakes is a global environmental problem that has negative environmental, economic and health impacts. Lake Ontario, the fourteenth largest lake in the world, supplies the drinking water for over nine million people. In recent decades, rapid urban growth (particularly in the Greater Toronto Area) has been accompanied by eutrophication along its western shoreline. Although in-situ measurements can provide reliable water quality data, frequent and spatially resolved monitoring is costly and laborious for such a large geographic area. In this study, 20 years (2000-2020) of satellite data (Sentinel 2 and Landsat 5, 7, and 8) are used to map chlorophyll-a (chl-a) concentrations, a proxy for phytoplankton biomass, in the western basin of Lake Ontario. To achieve this goal, a semi-empirical chl-a retrieval model is developed for the case study using more than 200 images and 206 in-situ match-up data for the calibration and validation process. To eliminate uncertainties due to atmospheric effects, we tested the following atmospheric correction (AC) processors: ACOLITE, ATCOR, C2RCC, DOS1, FLAASH, iCOR, QUAC, and Polymer. Then, various chl-a retrieval indicators including simple band ratios, normalized difference indexes, Ocean Color models and FLH-based algorithms were applied to each AC's output. In this presentation, we report the performance of each indicator based on the correlation analysis between in-situ chl-a concentrations and the remote sensing pixel values of the match-up locations.

https://agu.confex.com/agu/fm22/meetingapp.cgi/Paper/1152585