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

The bulk refractive index (np) 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 np remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the np in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, Bp, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, Rrs(λ). 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 (cp), scattering (bp), and backscattering (bbp) coefficients, total suspended matter (TSM) concentrations, and Rrs(λ). Based on those in situ observation data, two retrieval algorithms for TSM and bbp were firstly established from Rrs(λ), and then close empirical relationships between cp and bp with TSM could be constructed to determine the Bp and j parameters. The series of steps for the np estimation model proposed in this study can be summarized as follows: Rrs (λ) → TSM and bbp, TSM → bp → cp → j, bbp and bp → Bp, and j and Bp → np. This method shows a high degree of fit (R2 = 0.85) between the measured and modeled np by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R2 = 0.95, MRE = 2.32%). A spatial distribution pattern of np 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 np estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the np 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 np in other coastal optically complex water bodies.


Introduction
The bulk refractive index (np) 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 np 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 np 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 Twardowski et al. (2001) [2] model, i.e., modeling the np using the particulate backscattering ratio (Bp) and hyperbolic slope of the particle size distribution (j), Suresh et al. (2006) [23] developed an empirical band ratio method to model Bp and j and then further estimated the np in the Arabian Sea from Remote Sensing Satellite (IRS-P4), Ocean Color Monitor (OCM)satellite data. They found that the np values were low in the open ocean and relatively high in the coastal waters. Based on Mie theory, Nasiha et al. (2014) [4] developed a retrieval model to estimate the np to understand particulate assemblage dynamics in coastal waters. To apply this inversion model to satellite data, Nasiha et al. (2015) [5] further proposed a proof-of-concept method to derive the np 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 Bp, j, and particulate apparent density (ρa), which are finally used to calculate the refraction index.
Clearly, the np estimation models using remotely sensed data remain very limited. Additionally, the existing methods are mostly based on empirical relationships such as those between Rrs(λ) band ratios with the parameters Bp and j of Suresh et al., (2006) [23] and fitted empirical relationships between Rrs(λ) with turbidity and Bp [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 np estimation models should be developed to cope with different water conditions, especially for turbid coastal waters.
This study collects an adequate bio-optical dataset that covers the measurements of inherent optical properties, such as particulate backscattering (bbp), scattering (bp), and beam attenuation (cp) coefficients, remote sensing reflectance (Rrs(λ)) measurement, and total suspended matter (TSM) measurement. The investigated water areas of this study were the Bohai Sea and Yellow Sea. The objective of the current study was to develop a method for remote sensing estimation of np by using satellite ocean color data. This study proposes a multiple-step hybrid model that firstly depends on the TSM and bbp retrievals, then the established close relationships between bp and cp with TSM, and ultimately upon the Twardowski et al. (2001) [2] model derived from Mie theory calculations. By using independent datasets including in situ data and satellite retrievals, the proposed method is assessed, and then applied to Geostationary Ocean Color Imager (GOCI) data. The spatiotemporal distribution patterns of np are documented. At last, some necessary discussions are appended.

The Study Water Areas
The investigated water areas of this study were the marginal seas of the northwest Pacific Ocean along with China, i.e., the Bohai Sea (BS) and Yellow Sea (YS). The BS and YS are typically large shallow semi-enclosed seas with a water depth from several meters to about 100 m ( Figure 1). These seas are highly turbid and optically complex waters that are significantly influenced by terrigenous discharge. The datasets used in the present study were collected from four cruise surveys in November 2014, August 2015, July 2016, and January 2017 in the BS and YS ( Figure 1).

Bio-Optical Measurements
By using a profiling package, bio-optical measurements were performed to measure various optical parameters. The package included a Seabird ABE911P conductivity-temperature-depth (CTD) profiler that was used to measure hydrological characteristics of the water bodies and some optical devices, including a WET Labs AC-S and a HOBI Labs Hydroscat-6 (HS-6). The particulate absorption coefficient (ap(λ)) and beam attenuation coefficient (cp(λ)) in the spectral range of 400-700 nm were observed with the AC-S instrument, and then the particulate scattering coefficient (bp(λ)) could be derived using the relationship bp(λ) = cp(λ) − ap(λ). The particulate backscattering coefficient, bbp(λ), was observed by the HS-6 instrument which has six spectral channels, i.e., 442, 488, 550, 620, 700, and 852 nm, while the blue and green bands (i.e., 442, 488, and 550 nm) were used as delegates for the analysis in this study. The detailed measurement methods for the AC-S and HS-6 instruments can be seen in the Sun et al. (2016) [24] study.
The remote sensing reflectance (Rrs(λ)) spectra were collected with a Satlantic Hyper-Profiler II radiometer [25]. Meanwhile, water samples were simultaneously collected along with the above optical measurements and then filtered immediately in the lab onboard. The filtered particulates on GF/F filters were used to analyze total suspended matter (TSM) concentrations [24]. The collected bio-optical parameters during the four cruises are summarized in Table 1.

np Calculation
The bulk refractive index of suspended particles (np) was calculated using a Mie theory-based relationship model from the so-called input parameters, namely, the particulate backscattering ratio BP and the particle size distribution (PSD) slope j (Equation (1) Here, the Bp (= bbp/bp), bbp, and bp were measured by HS-6 and AC-S. The cp(λ) spectrum can be modeled using a hyperbolic power function [2,26,27].
where λref is a reference wavelength, and β is the spectral slope parameter. By using the cp at different wavelengths, we were accordingly able to calculate parameter β. According to Boss et al. [28], the parameter β is found to covary with the parameter j, which can be refined as the following equation for more PSD cases: Therefore, we can obtain the parameter j to calculate the np.

GOCI Data Collection and Processing
In this study, GOCI Level-1B data were used to assess the application of the developed refractive index (np) model. As a geostationary ocean color remote sensing satellite, GOCI receives images eight times a day from 12:15 a.m. to 7:45 a.m. Greenwich Mean Time (GMT) (8:15 to 15:15 local time) with 1-h temporal resolution. The GOCI satellite data have a spatial coverage of about 2500 km × 2500 km covering the northwest Pacific Ocean, a spatial resolution of 500 m, and eight spectral bands with the central wavelengths of 412, 443, 490, 555, 660, 680, 745, and 865 nm. In this study, a total of 2825 GOCI satellite images during the one-year period from December 2014 to November 2015 were downloaded from the Korea Ocean Satellite Center (KOSC). By utilizing the GOCI Data Processing System (GDPS, version 1.3), the image data focusing on the BS and YS regions were firstly extracted and then processed by the default atmospheric correction method of Wang & Gordon (1994) [29] to output Rrs(λ) data. These Rrs(λ) data were further quality controlled by using the various flags such as stray light and cloud coverage to avoid interference from invalid satellite data signals.

Development of np Estimation Model
Estimation of TSM is a key step in retrieving the final target, np, by Rrs(λ). This study firstly analyzed the correlation between single Rrs(λ) and TSM in the spectral range of 400-700 nm, the results of which showed that the red bands were the most sensitive to TSM vis-à-vis the other bands ( Figure 2). For instance, the correlation coefficients (R) showed high values (0.838 and 0.839) in the 660-nm and 680-nm bands of the GOCI image data, respectively. These findings also agree with those of previous studies [30,31]. These bands can be subsequently considered for use in establishing a TSM model. Empirical methods are simple and easy to model and, more importantly, straight and efficient, particularly for local regions [32]. Thus, this study made use of single bands, band ratios, and band combinations to develop the TSM model. Specifically, eight band forms, as shown in Table 2, were tested. For each band form, the correlation with TSM was examined for all possible combinations from six GOCI Rrc(λ) bands, and the optimal band combinations with the highest R are given in Table  2. After comparing the correlations among these band forms, we determined X7, i.e., (Rrs(555) + Rrs(660))/(Rrs(555)/Rrs(660)), with the highest R of 0.847 among those band forms for establishing the TSM model. This method to find the optimal band form and optimal band combination was similar to that used in previous studies [33,34]. Several mathematical methods, including linear, power, exponential, and logarithmic functions, were used to model TSM, and their accuracies were then intercompared to obtain a good retrieval model. The obtained results showed that the simple linear model continued to perform best (Figure 3), and it was subsequently used to estimate TSM in our whole model framework. Table 2. Correlations between TSM and band form X derived from GOCI (Geostationary Ocean Color Imager) six bands. X1 to X8 indicate eight band forms, respectively; R is the correlation coefficient.  (660)). The solid red lines are the fitted function curves, and the dotted red lines are the 95% confidence bounds.

Estimation of bp and p from R rs (λ)
Estimation of the particulate backscattering coefficient, bbp(λ), is another important step. Similar to TSM estimation, this study firstly carried out a correlation analysis and then demonstrated that the X7 band combination form was the best indicator for the bbp(λ) estimation ( Figure 4B). Note that the 488-nm channel was used to denote the parameter due to its better performance when compared to those of the other channels. After testing several different mathematical functions, we selected and established two strong relationships using the X7 form to model the bbp(488). As shown in Figure 4C, the fitted determination coefficient, R 2 , was 0.820 (p < 0.001), with relatively low predictive errors.  (660))) and bbp(488) using a power function; the solid red lines are the fitted function curves, and the dotted red lines are the 95% confidence bounds.

Derivation of the PSD Slope, j, from TSM
A close relationship between TSM and bp(488) could be established with relatively high fitting accuracy ( Figure 5A), which was also consistent with that demonstrated in the He et al. [32] study. Meanwhile, very close relationships between bp(λ) and cp(λ) could also be found in our study area, as observed in previous studies [5,[35][36][37]. As shown in Figure 5B, their relationships could be modeled well, with an extremely high R 2 of 0.998 (p < 0.001). Such good relationships provide a stable basis for accurately deriving cp(λ) at different wavelengths from TSM. According to Equations (2) and (3), we obtained the parameter j, which was then used as an input of the model estimating np in this study. As a summary, Figure 6 shows the main processes of our developed model in the current study to estimate the bulk refractive index np of particles by Rrs(λ). A Mie-theory-based relationship model was essentially adopted to derive np values from the so-called input parameters, namely, BP and j (Equation (1) by Twardowski et al. (2001) [2]), considering the fact that there are currently hardly any methods that can be used to directly measure the particle bulk refraction. Although some empirical relationships were used in the framework of our model development, the proposed np estimation model is a meaningful attempt to obtain satellite-derived np records. Figure 6. Flow diagram of our developed np estimation model that shows the steps of deriving np from Rrs(λ). The shown different colors in the legend refer to different processes, namely, inputs, calculations, models, and outputs.

Performance Metrics
To evaluate the models' performances, this study utilized the root-mean-square error (RMSE), mean absolute error (MAE), mean relative error (MRE), determination coefficient (R 2 ), and relative bias (Bias). These indicators can be calculated as follows: where yi and yi' are the measured and predictive values for the i-th sample, respectively, and N represents the total number of samples.

Water Bio-Optical Conditions
The bio-optical parameters in our investigated water regions showed very large dynamic ranges, which were collected from the four cruises (Figure 7). TSM varied from 0.6 to 223.9 mg•L −1 and had a mean of 11.2 ± 22. 7 mg•L −1 . The large variation coefficient (CV)value (202.51%) indicates its large variation. The particulate backscattering coefficient at 488 nm, bbp (488), ranged between ~0 and 1.21 m −1 , had a mean of 0.19 ± 0.17 m −1 , and had a large CV (141.7%). Correspondingly, the particulate scattering and beam attenuation coefficients, bp(488), cp(532), and cp(555), also showed wide variation ranges, as well as large CV values. The particulate backscattering ratio Bp(488), i.e., bbp/bp, changed from ~0 to 0.10 when the PSD slope, j, was observed to be in the range of 3.2-5.0. Note that a large span also appeared for the bulk refractive index of particles np. Meanwhile, the collected in situ Rrs(λ) showed large variations in terms of both magnitude and spectral shapes (Figure 8). These Rrs(λ) spectra had similar spectral properties to those seen in previous reports on coastal waters, for example, the studies of He et al. (2013) [32] and Sun et al. (2014) [36], which indicate typical optically complex turbid water conditions in the study area.

Validation of np Estimation Model
To evaluate the performance of the developed np model, we firstly used an independent validation dataset with 24 samples collected from the BS and YS in January 2017. As shown in Figure  9A, the modeled and in situ np values generally showed good clustering along the 1:1 line, with relatively low predictive errors. Most of the data points (approximately 84% percentage) were distributed within the ±5% range. On the other hand, this study carried out satellite synchronization verification. After matching GOCI satellite-derived Rrs data with in situ measurements using a set of strict constraints, a limited seven synchronous samples (±1 h) were obtained for use in comparisons between the in situ measured and satellite-derived np. Figure 9B shows the generally good agreement between them, with very low predictive errors (such as MRE = 2.32%), despite the modeled values being on the high side to some extent. This implies that the developed np model has the potential ability to produce accurate and acceptable results.
Additionally, we compared the spatial distributions of particle bulk refraction produced by in situ measurements and satellite derivation. Note that the satellite data used were from the specific monthly coverage concurrent with the cruise in January 2017. Figures 9C,D show the spatial patterns derived by in situ observations and satellite retrievals, respectively. Although there was no absolute real-time synchronization between the in situ measured and satellite-derived monthly np, the cruise sampling covered a period of nearly one month and was able to roughly provide an accurate reference. The generally similar spatial distributions of the in situ observations and satellite retrievals imply the accuracy of our developed np model.

Model Application to Satellite Data
We could produce hourly np products by applying the developed model into the hourly GOCI Rrs(λ) data. Those hourly products were then synthesized into daily, monthly, seasonally, and annually averaged products. As shown in Figure 10, the general distribution pattern for 2015 is available. High np values generally dominated nearshore waters, while low values were mainly distributed in the offshore waters. The whole BS region usually had relatively high refraction values of approximately >1.10, whereas most areas in the YS, except coastal regions, showed low values (roughly <1.05). Note that a large water region that includes Subei shoal and the Yangtze River estuary appeared to have very high refraction, which even exceeded 1.15 for some areas. The seasonal and monthly variations in np in this study water areas were further generated as shown in Figure 11. On the whole, the np during the period from December 2014 to April 2015, as well as November 2015, appeared to have the most high-valued distribution throughout the entirety of the BS and YS regions, whereas relatively low values showed up between May and October 2015. In detail, in several particular water regions, including the Yellow River estuary, Yangtze River estuary, Subei shoal, and very nearshore areas, the np values remained almost static over time and were always high (>1.15) throughout the entire year. In most regions of the BS, the np values showed distinct seasonal variations, namely, high values in winter, early spring, and late autumn, whereas the values in summer were low. Similar temporal trends in np variation also appeared in the YS. However, the difference with that in the BS was that there existed generally lower np values in the YS for the same month.

Advantages and Disadvantages of the np Model
The np model proposed in this study is a proof-of-concept remote sensing method for retrieving particulate refraction from GOCI satellite measurements in the study area. It is essentially dependent on the accurate retrievals of TSM and bbp, which are achieved by establishing stable empirical models. The variety of field measurements from the four cruises represents a prominent advantage in this study and provides a steady basis for developing the TSM and bbp empirical models. As shown in Figure 12, a leave-one-out cross-validation (LOO-CV) [38,39] was conducted to evaluate the performances of the TSM and bbp models. Briefly, we randomly selected one sample from the full dataset (sample number, n) that served for the validation of the model, while the remaining n − 1 samples were trained for model calibration. Based on the LOO-CV method, all samples were tried for one round, deriving relatively low and acceptable predictive errors. Thus, relatively accurate TSM and bbp retrievals, as well as their close relationships with bp and cp, provided a feasible route for achieving the estimation of np in the study areas. Another advantage lies in that there are more adequate remote sensing signals for model inputs when compared with the remote sensing method proposed by Nasiha et al. (2015) [5], which utilizes a single band reflectance (i.e., a green band at 551 nm) to derive the intermediate and target parameters. In addition to green band reflectance (555 nm), this study introduced red band reflectance, i.e., 660 nm, as another input, which assures more adequate valid remote sensing information that could potentially derive better outputs. The developed np model may show limitations when it is applied to other coastal water areas. This is due to different optical properties that may vary with the change of study area because of the diversity and dynamics of in-water constituents [24,[40][41][42][43][44]. Such local characteristics on water conditions generally determine region-specific empirical relationships between TSM (and bbp) and remote sensing reflectance and between bp (and cp) and TSM [16,17,45,46]. Nevertheless, these socalled limitations should not hinder the use of the proof-of-concept method proposed in this study for other regions. The same method with necessary local parameterizations can be employed to derive the particulate refraction using remote sensing data only if valid regional data can be obtained. However, in future, detailed investigations are still required to thoroughly assess the model's applicability in various coastal water conditions. In addition, this study mainly focused on developing a method for estimating np from satellite data; thus, we used GOCI data as a case study. The applicability of the method to other satellite sensors is not clear, and further studies are also required to apply the similar method of this study to develop np estimation models specifically for MODIS, Visible infrared Imaging Radiometer (VIIRS), Landsat, etc., according to their band specifications, and then compare and cross-validate their performances.

Comparison with the Method Using a Straight Bp Empirical Model
The particulate backscattering ratio, Bp, is a straight input parameter to calculate np. In our developed np model, this parameter was obtained through the ratio by the modeled bbp and bp. However, we may still have another alternative, i.e., developing a Bp empirical model. By employing steps similar to those in Sections 2.5.1 and 2.5.2, we developed an empirical relationship between Bp(488) and X8 that performed better than other functional models (see Figure 13A). By using the new method nested with the Bp empirical model, this study then obtained new np results, which showed roughly similar performance with that of the previous method ( Figure 13B). However, note that the np spatial distribution derived by the new method was not very desirable, as there existed obvious overestimations for the offshore areas ( Figure 13C). This was probably due to the fitted overempirical relationship between Rrs and Bp of Yang et al., (2011) [47] because there is no physical linkage between them in theory, considering that Bp (= bbp/bp), especially bp therein, is essentially not retrievable from remote sensing owing to the fact that remote sensing covers no information on the forward scattering of suspended particles by Sun et al., (2016) [24]. By comparison, our proposed np retrieval method still showed superior performance in the study water areas.

Driving Factors of np Spatiotemporal Variation
By applying the developed np model to time-series GOCI satellite data, the np spatiotemporal variations in the study areas could be obtained, with the derived values spanning a range of 1.01-1.22 (Figures 10 and 11). This variation in magnitude is generally consistent with that reported in previous studies [2,5,23,48,49]. In general, the np in the nearshore regions was higher than that offshore, noting that the nearshore regions showed relatively stable high values, whereas the offshore areas changed a lot during different seasons (or months). High-content mineral particles in the nearshore waters (e.g., the Yellow River estuary and Yangtze River estuary) induced by terrestrial discharge, sediment resuspension, and even shore erosion can explain the high np (1.12-1.18, see Figure 10) distribution there.
Regarding the offshore areas, the np values in the BS were higher than those in the YS. This is because the BS is prone to sediment resuspension under the action of wind waves due to its relatively shallow depths [50,51]. Song et al. (2014) [52] demonstrated that inorganic minerals and silts controlled the suspended particles in the BS. Therefore, those hard mineral and sediment particles may explain the high np values in the BS (except those nearshore regions), which were mainly in the range of 1.10-1.15 ( Figure 10) [2,5,53]. Fortunately, the derived np distributions generally agree with the previous report [5]. By contrast, the YS waters (except those nearshore regions) are easily affected by algal particles, where diatoms and dinoflagellates are the main algal species [54][55][56]. Importantly, these algal particles generally show a relatively low bulk refraction (approximately 1.01-1.07) due to their high water content [57][58][59]. Therefore, the low refraction distribution in most regions of the YS (i.e., np < 1.07, see Figure 10) can be attributed to the dominance of organic algal particles.
From the perspective of the seasonal variations, the np showed the lowest distribution in summer for most areas of the BS and YS, whereas the highest distribution appeared in winter. The seasonal variations are probably related to water column mixing and stratification, as well as phytoplankton growth and extinction during different seasons [60,61]. Actually, the factors influencing the np distribution could be very complex, which may be linked with many marine physical and biogeochemical processes such as wind waves, river discharge, hydrodynamic conditions, water column mixing and stratification, sediment suspension, phytoplankton growth, and zooplankton grazing. In short, the precise understanding on the mechanisms influencing the np spatiotemporal variations remains challenging owing to the coupling influence of these multiple factors.

Implications for Marine Environmental Changes
The bulk refractive index of suspended particles (np) is a key parameter that reflects the intrinsic characteristics of suspended particles, is closely related to the particulate composition and size structures [2], and significantly affects the optical properties of suspended particles. Actually, different np distribution ranges are indicative of different types of suspended particles present in water bodies such as 1.01-1.09 mainly for organic algal particles, >1.15 for water regions dominated by inorganic particles and detritus, and 1.09-1.15 for complex and mixed particulates [2,3,48,49]. Satellite-derived np values in the study areas spanned a wide range (approximately 1.01-1.22), thus implying a large spatial heterogeneity. Three types of water regions can be generalized as (1) offshore oceanic water areas dominated by phytoplankton, (2) river plume and sediment-laden nearshore regions dominated by inorganic mineral particles, and (3) coastal regions controlled by organic and inorganic mixed materials. Although no in situ method exists for directly measuring the refractive index of suspended particles, the validation revealed the robustness of the currently proposed method to detect np for the study areas and further distinguish waters dominated by different types of suspended particles. In short, the spatiotemporal detection of np provides a new data record that enriches suspended particulate properties from space, and importantly improves understanding of the particulate biogeochemical properties and their role in exploring the changes of marine environments.

Conclusions
The current study proposed and validated a multiple-step hybrid remote sensing method to estimate np in the BS and YS from GOCI satellite measurements. This np estimation model depends on three crucial steps, namely, (1) the TSM and bbp retrievals, (2) establishing close relationships between bp and cp with TSM, and (3) utilizing the Twardowski et al. (2001) [2] model derived from Mie theory. The first two steps were calibrated and validated using a large quantity of in situ observed data from four cruises in the study area, whereas the third step was directly utilized, as it is widely recognized by the community. These steps jointly constructed our developed np estimation model in this study, which led to reliable performances with reasonable uncertainties (2.55% and 2.32% for MRE, validated using in situ observations with the leave-one-out cross-validation method and satellite-derived results, respectively) and spatial distribution patterns. By means of 2825 GOCI satellite images collected during one year from December 2014 to November 2015, this np estimation model documented np distribution patterns in the BS and YS at different temporal scales for the first time, such as monthly, seasonal, and annual scales. Although this method is possibly region-specific and may need more validation for its application to other water areas, the developed method here provides a proof-of-concept template for np remote sensing estimation in other coastal turbid water bodies.
Author Contributions: Deyong Sun designed this study and the algorithm; Zunbin Ling and Shengqiang Wang contributed to the data analyses and drafted the manuscript; Zhongfeng Qiu and Yu Huan assisted with developing the research design and results interpretation; Zhihua Mao and Yijun He contributed to the interpretation of results and manuscript revision. All authors have read and agreed to the published version of the manuscript.