Estimating the Bulk Refractive Index and Related Particulate Properties of Natural Waters From Remote-Sensing Data

TLDR: A proof-of-concept method to estimate bulk refractive index of particles for deriving information on their composition and optical properties of natural waters using satellite ocean color data provides new tools for improving current understanding of particle dynamics of pelagic ecosystems, advancing biogeochemical modeling, assessing the global ocean ecosystem, and elucidating the coastal processes.

Abstract: A proof-of-concept method is developed to estimate bulk refractive index ( ${{{\eta }}_{{\bm {bp}}}}$ ) of particles for deriving information on their composition and optical properties of natural waters using satellite ocean color data. It estimates ${{{\eta }}_{{\bm {bp}}}}$ as a function of the backscattering ratio ( ${{\bm {B}}_{\bm {p}}}$ ), hyperbolic slope of the particle size distribution ( ${{\xi }}$ derived from the beam attenuation slope ${{\beta }}$ ), and apparent density of particles ( ${{{\rho }}_{\bm {a}}}$ ) as described in our previous inversion model. For deriving these parameters from remote-sensing data, large in situ measurement data of the remote-sensing reflectance ( ${{\bm {R}}_{{\bm {rs}}}}$ ), particulate scattering ( ${{\bm {b}}_{\bm {p}}}$ ), backscattering ( ${{\bm {b}}_{{\bm {bp}}}}$ ) beam attenuation ( ${{\bm {c}}_{\bm {p}}}$ ), and seawater constituents’ concentrations are used to establish the required relationships and derive the model parameters (i.e., ${{{\eta }}_{{\bm {bp}}}} \leftarrow {{\bm {B}}_{\bm {p}}}$ , ${{\xi }}$ , ${{{\rho }}_{\bm {a}}}$ ; ${{\bm {B}}_{\bm {p}}} \leftarrow {{\bm {R}}_{{\bm {rs}}}}$ ; ${{\xi }} \leftarrow {{\beta }} \leftarrow {{\bm {c}}_{\bm {p}}} \leftarrow {\bf Turbidity}$ ; ${\bf Turbidity} \leftarrow {{\bm {R}}_{{\bm {rs}}}}$ ; and ${{{\rho }}_{\bm {a}}} \leftarrow {{\bm {c}}_{\bm {p}}}$ ). When validated with those derived from in situ data spanning the large dynamic range in water optical properties and relevant water constituents impacting the remote-sensing signal, the derived model parameters and bulk refractive index had errors of a few percent which falls well within the benchmark for a validated uncertainty of ${\rm \pm }{\bf 35}\% $ endorsed for the remote-sensing retrieval of chlorophyll- $a$ in oceanic waters. The model was applied to regional and global images of satellite ocean color observations provided by the MODIS-Aqua sensor. Global images of seasonal climatology over a decade of MODIS-Aqua ocean color observations provided interesting insights into the seasonal variability of ${{{\eta }}_{{\bm {bp}}}}$ in coastal and open ocean waters. To further investigate the bulk particulate assemblages in some regional waters, several MODIS-Aqua imageries from different regional waters dominated by sediment-laden river plumes and phytoplankton blooms were analyzed. Satellite retrievals of the ${{{\eta }}_{{\bm {bp}}}}$ varied from 1.035 to 1.05 for clear oceanic waters, 1.045–1.07 for phytoplankton-dominated waters (higher water content), 1.05–1.15 for floating algal blooms and Cocolithophore blooms, 1.07–1.10 for coastal waters dominated by detrital particles, and 1.10–1.28 for river plume and sediment-laden coastal waters (lower water content). These results were consistent with those expected for the waters examined, being in excellent agreement with the previous reports and estimates based on the laboratory/ in situ measurements and modeling studies. Given the fact that there is currently no satellite-based method to differentiate between bulk particulate compositions, the new method provides a valuable tool for estimating ${{{\eta }}_{{\bm {bp}}}}$ values as a proxy for the apparent density and composition of particles and such information when derived from satellite ocean color data provides new tools for improving our current understanding of particle dynamics of pelagic ecosystems, advancing biogeochemical modeling, assessing the global ocean ecosystem, and elucidating the coastal processes.