Abstract

The paper draws on two different reflective band-ratio algorithms, namely the Maximum Chlorophyll-a Index (MCI) and New Three Band Algorithm (N3B) to estimate Chlorophyll-a (Chl-a) concentrations from Landsat-8 images and spectrometric water samples. Band tuning procedures was performed to find optimal peak wavelengths suitable for the estimation of Chl-a from Landsat-8 satellite image and spectrometric data. Additionally, the MCI and N3B were applied on both in-situ and Landsat-8 data and compared using statistical regression models such as the coefficient of determination (R²), relative mean absolute error (rMAE), and root mean square error (RMSE) to find the best performing algorithm in estimating Chl-a pigments. The results demonstrates that the MCI algorithm performed sensitively in the estimation of Chl-a as compared to the N3B, after data regression. The MCI algorithm obtained a higher R² of 0.69, with a minimal percentage error (rMAE) of 18.34% and RMSE of 1.85 m^-1 when applied on in-situ data. A similar result was obtained when MCI was applied on Landsat-8 data with a higher R² of 0.75 and a minimal percentage error (rMAE) of 21.29% and an RMSE of 0.97 m^-1, respectively. However, the N3B algorithm returned a lower R² of 0.54 and 0.65 when applied on both in-situ and Landsat-8 data concurrently. The standard errors for MCI were comparatively lower than that of the N3B. Hence, in this study, the MCI algorithm performed better because it has less predictive error. In all, although both algorithms were able to estimate Chl-a pigments, the MCI algorithm is more sensitive in the retrieval of Chl-a concentration from Case-2 inland waters using both in-situ and Landsat-8 data. The results indicate the high potential of analytical algorithms to estimate Chl-a concentration in turbid and eutrophic productive (Case II) waters using satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland aquatic ecosystems.

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