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Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow><mo>,</mo><mo> </mo><mi>s</mi><msup><mi>r</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula> to Inherent Optical Properties (IOPs) of an aquatic medium (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>n</mi><mi>w</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow><mo>,</mo><mo> </mo><msup><mo>m</mo><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula> (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi>a</mi><mrow><mi>p</mi><mi>h</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow><mo>,</mo><mo> </mo><msup><mo>m</mo><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula> and coloured detrital matter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi>a</mi><mrow><mi>d</mi><mi>g</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow><mo>,</mo><mo> </mo><msup><mo>m</mo><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula>. 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> and explores potential alternatives to operational SAAs. Using <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>n</mi><mi>w</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula>. Among these three models, QAA and GIOP models derived <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>a</mi><mrow><mi>n</mi><mi>w</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> 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, GIOP_GSCM, GIOP_Zhang, QAA_GSCM and QAA_Zhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula>. GIOP_GSCM and GIOP_Zhang 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.