Hybrid Inversion Algorithms for Retrieval of Absorption Subcomponents from Ocean Colour Remote Sensing Reflectance

Abstract

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance (Rrs(λ), sr−1) to Inherent Optical Properties (IOPs) of an aquatic medium (λ is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition anw(λ), m−1 (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton (aph(λ), m−1) and coloured detrital matter (adg(λ), m−1). Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from Rrs(λ) and explores potential alternatives to operational SAAs. Using Rrs(λ) and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving anw(λ) from Rrs(λ). Among these three models, QAA and GIOP models derived anw(λ) with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang’s model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOPGSCM, GIOPZhang, QAAGSCM and QAAZhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from Rrs(λ). GIOPGSCM and GIOPZhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed.

Decomposition of the total non-water absorption coefficient for retrieval of absorption subcomponents

“…Many semi-analytical algorithms (SAA) have been developed to derive IOPs with various methodologies from 𝑅 𝑟𝑠 (𝜆), which may be grouped into two types: 1) simultaneous inversion of 𝑅 𝑟𝑠 to IOP subcomponents such as absorption of phytoplankton, gelbstoff, detritus and particle backscattering using a radiative transfer approximation (RTA) with solution determined with nonlinear or linear matrix optimization methods 4,7 , and 2) step-wise decomposition using a combination of empirical and analytical relations for deriving 𝑎(𝜆), 𝑏 𝑏 (𝜆) and subcomponents from 𝑅 𝑟𝑠 (𝜆) 3,[8][9][10] .…”