A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

Featured Application The study proposed a multiple-step hybrid remote sensing method to estimate bulk refractive index (n(p)) of suspended particles in the Bohai Sea and Yellow Sea from GOCI satellite measurements. This proposed method can be applied to study the spatial and temporal variations of n(p) in the Bohai Sea and Yellow Sea, and thereby to understand the particulate biogeochemical properties (e.g., composition and size) and their role in exploring the changes of marine environments. Abstract The bulk refractive index (n(p)) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine waters. Remote estimation of n(p) remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the n(p) in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, B-p, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, R-rs(lambda). The in situ observed datasets available to us were collected from four cruise surveys during a period from 2014 to 2017 in the BS and YS, covering beam attenuation (c(p)), scattering (b(p)), and backscattering (b(bp)) coefficients, total suspended matter (TSM) concentrations, and R-rs(lambda). Based on those in situ observation data, two retrieval algorithms for TSM and b(bp) were firstly established from R-rs(lambda), and then close empirical relationships between c(p) and b(p) with TSM could be constructed to determine the B-p and j parameters. The series of steps for the n(p) estimation model proposed in this study can be summarized as follows: R-rs (lambda) -> TSM and b(bp), TSM -> b(p) -> cp -> j, b(bp) and b(p) -> B-p, and j and B-p -> n(p). This method shows a high degree of fit (R-2 = 0.85) between the measured and modeled n(p) by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R-2 = 0.95, MRE = 2.32%). A spatial distribution pattern of n(p) in January 2017 derived from GOCI (Geostationary Ocean Color Imager) data agrees well with those in situ observations. This also verifies the satisfactory performance of our developed n(p) estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the n(p) spatial distribution patterns at different time scales (such as monthly, seasonal, and annual scales) for the first time in the study areas. While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate n(p) in other coastal optically complex water bodies.