Effect of optically active substances and atmospheric correction schemes on remote-sensing reflectance at a coastal site off Kochi

Abstract

The present study focused on understanding the variability of optically active substances (OASs) and their effect on spectral remote-sensing reflectance (Rrs). Furthermore, the effect of atmospheric correction schemes on the retrieval of chlorophyll-a (chl-a) from satellite data was also analysed. The OASs considered here are chl-a, coloured dissolved organic matter (CDOM), and total suspended matter (TSM). Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite was used for this study. The two atmospheric correction schemes considered were: multi-scattering with two-band model selection NIR correction (hereon referred as ‘A1’) and Management Unit of the North Sea Mathematical Models (MUMM) correction and MUMM NIR calculation (hereafter referred as ‘A2’). The default MODIS bio-optical algorithm (OC3M) was used for the retrieval of chl-a. Analysis of OASs showed that chl-a was the major light-absorbing component, with highly variable distribution (0.006–25.85 mg m–3). Absorption due to CDOM at 440 nm (aCDOM440) varied from 0.002 to 0.31 m–1 whereas TSM varied from 0.005 to 33.44 mg l–1. The highest concentration of chl-a was observed from August to November (i.e. end of the southwest monsoon and beginning of the northeast monsoon), which was attributed to coastal upwelling. The average value of aCDOM440 was found to be lower than the global mean. A significant negative relationship between aCDOM440 and salinity during the southwest monsoon indicated that much of the CDOM during this season was derived from river discharge. Spectral Rrs was found to be strongly linked to the variability in chl-a concentration, indicating that chl-a was the major light-absorbing component. Satellite-derived spectral Rrs was in good agreement with that in situ when chl-a concentration was lower than 5 mg m–3. The validation of chl-a, derived from in situ Rrs, showed moderate performance (correlation coefficient, R2 = 0.64; log10(RMSE) = 0.434; absolute percentage difference (APD) = 43.6% and relative percentage difference (RPD) = 42.33%). However the accuracy of the algorithm was still within acceptable limits. The statistical analysis for atmospheric correction schemes showed improved mean ratio of measured to estimated chl-a (‘r’ = 1.6), log10(RMSE) (0.49), APD (25.46%), and RPD (17.57%) in the case of A1 as compared with A2, whereas in the case of A2, R2 (0.56), slope (0.26), and intercept (0.27) were better as compared with A1. The two atmospheric correction schemes did not show any significant statistical difference. However the default atmospheric correction scheme (A1) was found to be performing comparatively better probably due to the fact that the concentration of TSM and CDOM was much lower to overcome the impact of chl-a.