Subcomponent inherent optical properties retrieval from total absorption coefficient and remote sensing reflectance measured in coastal waters

The present study evaluates the applicability of two absorption decomposition algorithms (ADA) (Zhang and Lin's models) in the retrieval of subcomponent Inherent Optical Properties (IOPs) from coastal waters. These ADAs use measured or model-derived total non-water absorption coefficient a(nw)(lambda) (total absorption coefficient subtracted water absorption coefficient) as input and provide absorption subcomponents - absorption due to phytoplankton a(ph)(lambda) and coloured detrital matter, a(dg)(lambda) as outputs. Coast Colour Round Robin match-up dataset, NASA's bio-Optical Marine Algorithm Dataset (NOMAD) and Kochi (Indian coastal waters) datasets that consist of coincident measurements of remote sensing reflectance, R-rs(lambda)(sr(-1)) and IOPs were used for the evaluation of various models. With measured a(nw)(lambda) as input, both Zhang and Lin's models demonstrated good performance with an average spectral mean absolute percentage error (MAPE) in the range of 19-44% for the derived absorption subcomponents for all three datasets. Quasi-Analytical Algorithm (QAA) and LS2 are two semi-analytical algorithms (SAAs) that use R-rs(lambda) as input and provide a(nw)(lambda) as output. The QAA-Zhang, QAA-Lin, LS2-Zhang and LS2-Lin models (combination of SAA and ADA) resulted in higher average spectral MAPE (33-59%) values for the derived absorption subcomponents as compared to other existing SAAs, owing to errors present in both SAAs and ADAs. These results indicate that improved SAAs are required to derive accurate a(nw)(lambda) to improve the applicability of ADAs in remote sensing applications.