## 1 Introduction  
The bulk refractive index (\(n_{\text{p}}\)) of suspended particles in natural waters is a critical physical parameter that describes particulate intrinsic properties such as material composition, shape, texture, and structure [1; 2; 3; 4; 5]. This parameter carries much critical information that significantly supports the research on the underwater light properties of particulate assemblages, and it is closely related to many marine ecological and biogeochemical processes and is, thus, capable of contributing to the knowledge of regional and even global ocean ecosystems [4; 6]. Consequently, acquiring \(n_{\text{p}}\) information, such as its spatiotemporal distribution, is of great significance to us. At present, several measurement methods for the bulk refractive index of suspended particles are available to us, such as the laser diffraction method, electrical resistance technique, and flow cytometry analysis [7; 8; 9]. These methods show relatively high observational accuracy and yet are time-consuming and laborious and need a large quantity of water sample collections. More importantly, they struggle to provide variations in large-scale synoptic and temporal distributions. Actually, the \(n_{\text{p}}\) data records remain scarce, especially for large-scale marine waters. The remote sensing technique provides a possibility for filling this gap. A large quantity of algorithms was developed for remote sensing estimation on water condition parameters such as chlorophyll a [10; 11; 12; 13; 14], suspended particulate matter [15; 16; 17], turbidity [18; 19], and even colored dissolved organic matter [20; 21; 22]. However, it is unfortunate that fewer algorithms were developed to derive the bulk refractive index of suspended particles. Based on the [PERSON] et al. (2001) [2] model, i.e., modeling the \(n_{\text{p}}\) using the particulate backscattering ratio (\(B_{\text{p}}\)) and hyperbolic slope of the particle size distribution (\(j\)), [PERSON] et al. (2006) [23] developed an empirical band ratio method to model \(B_{\text{p}}\) and \(j\) and then further estimated the \(n_{\text{p}}\) in the Arabian Sea from Remote Sensing Satellite (IRS-P4), Ocean Color Monitor (OCM)satellite data. They found that the \(n_{\text{p}}\) values were low in the open ocean and relatively high in the coastal waters. Based on Mie theory, [PERSON] et al. (2014) [4] developed a retrieval model to estimate the \(n_{\text{p}}\) to understand particulate assemblage dynamics in coastal waters. To apply this inversion model to satellite data, [PERSON] et al. (2015) [5] further proposed a proof-of-concept method to derive the \(n_{\text{p}}\) from Moderate-Resolution Imagine Spectroradiometer (MODIS)-Aqua images. This method firstly uses an empirical relationship to estimate turbidity based on single green band (551 nm) reflectance and then deduces the parameters, including \(B_{\text{p}}\), \(j\), and particulate apparent density (\(\rho_{\text{a}}\)), which are finally used to calculate the refraction index. Clearly, the \(n_{\text{p}}\) estimation models using remotely sensed data remain very limited. Additionally, the existing methods are mostly based on empirical relationships such as those between \(R_{\text{rs}}(\lambda)\) band ratios with the parameters \(B_{\text{p}}\) and \(j\) of [PERSON] et al., (2006) [23] and fitted empirical relationships between \(R_{\text{rs}}(\lambda)\) with turbidity and \(B_{\text{p}}\)[5]. Although case studies showed good performance, the lack of a necessary physical basis still limits the use of these methods, despite their proof of concept. Therefore, more validation should be undertaken to evaluate the previous limited methods for application to other water areas of interest. Importantly, new \(n_{\text{p}}\) estimation models should be developed to cope with different water conditions, especially for turbid coastal waters.