Application of Landsat-8 and Sentinel-2 for retrieval of chlorophyll-a in a shallow freshwater lake

Abstract

Remote sensing is a cost-effective method for monitoring chlorophyll-a (Chl-a) concentration, an indicator of eutrophication, due to its spatiotemporal effectiveness and availability of historical data. However, its application in shallow, small water bodies poses challenges due to the need for high spatial and temporal resolutions, significant optical interference from other water constituents, and depth-related accuracy issues. This study assessed the performance of Landsat-8 and Sentinel-2 satellites in estimating Chl-a concentrations in Chitgar Lake, Tehran. Semi-empirical models, constructed with 73 and 122 common data pairs between observed data and Landsat-8 and Sentinel-2 reflectance data, respectively, yielded accurate estimates. Two Band (2BAND) algorithm (green and red band ratio, R2 = 0.8, RMSE (root mean square error) = 1.12 µg.L-1 and NRMSE (normalized root mean square error) = 12.4 %) was identified as the best Landsat-8 based model. Both Normalized Difference Chlorophyll Index (NDCI) algorithm (red and red edge bands composition, R2 = 0.82, RMSE = 1.29 µg.L-1, NRMSE = 7.8 %) and 2BAND algorithm (red and red edge band ratio, R2 = 0.81, RMSE = 1.28 µg.L-1, and NRMSE = 7.7 %) were the best models for Sentinel-2 data, with a Power of 0.8. Overall, remote sensing data from both satellites demonstrated appropriate performance and can accurately estimate Chl-a concentrations in Chitgar Lake as a shallow freshwater body.