Dynamic estuarine Chlorophyll-a estimation-based time series harmonized Landsat- Sentinel images

This research develops a vigorous approach to estimate Chlorophyll-a (Chl-a) concentration in the dynamic, optically complex waters (or Case 2 water including coastal waters, estuaries and inland water bodies) of Ganh Rai Bay, Vietnam by leveraging time series harmonized Landsat and Sentinel-2 (TM-HLS) imagery. One of the fundamental challenges while conducting this method to compute Chl-a signal is noise caused by suspended sediments (TSS) and coloured dissolved organic matter (CDOM). Thus, the study utilizes spectral features (such as Blue/Green and Red/Green ratios) to correct TSS and CDOM interference, a critical step for Case 2 waters. Otherwise, by learning spectral correlation between different bands of the TM-HLS products and in-situ Chl-a lab-computed data, several log-transformed algorithms of selected bands were investigated to examine their efficiency in estimating Chl-a content. Results showed that the log-level regression model was the most effective which yielded a high coefficient of determination (R2 = 0.888) and a minimal standard error (SE = 0.06). Furthermore, the spatial distribution analysis, utilizing the log-level model, revealed that the Chl-a concentration was highly variable in coastal area (13-15mg/m3) due to river discharge and the semi-diurnal tidal regime, but more stable offshore. The HLS data set is confirmed to be effective for continuous spatiotemporal water quality assessment.