## 3 Comparative Retrieval of Suspended Sediment  
### Atmospheric Correction and Inversion Model Establishment  
Because neither the HJ CCD nor the GOCI sensors include a shortwave infrared (SWIR) band, they cannot effectively retrieve aerosol optical depth (AOD) over turbid waters. In contrast, MODIS, with its two SWIR bands and high sensitivity, can simultaneously derive aerosol type and AOD. In this study, the imaging times for the HJ CCD, GOCI, and MODIS were 10:29 a.m., 10:39 a.m., and 10:05 a.m., respectively, with time differences within approximately 30 min. Consequently, aerosol data retrieved from MODIS imagery were used to perform atmospheric correction for both the HJ CCD and GOCI images. This approach partially overcomes the reliance of radiative transfer model-based atmospheric correction on synchronous ground aerosol measurements [31; 32]. Moreover, as the two images differ by only 10 min, the SSC in Hangzhou Bay was assumed to remain relatively stable during the observation period. In fact, the analysis of continuous in situ observation data at station S02 showed that the suspended particle concentration increased from 1029.4 mg/L at 10:25 on 12 December 2011 to 1150.4 mg/L at 11:25, with an average 10 min change of only 1.95%, which proves that the fluctuation of suspended particle concentration within the 10 min offset between HJ-CCD and GOCI is negligible for this comparison. Therefore, the aerosol information derived from Terra/MODIS in the morning is considered applicable as an input parameter for the atmospheric correction of the temporally adjacent HJ CCD and GOCI images over the same region.  
Based on the in situ remote sensing reflectance data and SSC measurements, inversion models were developed by calculating the equivalent remote sensing reflectance for the corresponding bands of the HJ CCD and GOCI images. Single-band, band-ratio, and multi-band remote sensing factors were constructed. Two-thirds of the valid in situ data (33 points) were used to establish the models, and one-third (16 points) were reserved for validation. Regression analysis was then employed to develop inversion models relating the remote sensing factors to SSC, and the models were evaluated by the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and mean relative error (MRE) between the modeled and measured SSC values. Ultimately, the optimal inversion models for the HJ CCD and GOCI images were obtained.  
By comprehensive comparison, the optimal SSC inversion model for Hangzhou Bay based on the HJ CCD data was derived as follows [20]:  
\[\text{SSC}=13.895*\text{exp}\ (4.5176\ *\ \text{X}) \tag{1}\]  
where SSC denotes the suspended sediment concentration (mg/L), X = Rrs(b4)/Rrs(b3), and Rrs(b4) and Rrs(b3) represent the remote sensing reflectance of the fourth and third bands, respectively. An F-test confirmed that the regression was statistically significant at the 0.01 level, with an R\({}^{2}\) of 0.90 and an MRE of 13.60%. Furthermore, the atmospheric correction results for the third and fourth bands of the HJ CCD (ranging from 0.63 to 0.69 and 0.76 to 0.90, respectively) meet the inversion requirements, with relative errors of 18.99% and 12.60%. For the Hangzhou Bay GOCI image, the SSC inversion model is given by the following formula:  
\[\text{SSC}=30.795*\text{exp}\ (3.9748\ *\ \text{X}) \tag{2}\]  
where X = Rrs(b8)/Rrs(b6), and Rrs(b8) and Rrs(b6) denote the remote sensing reflectance of the eighth and sixth bands, respectively. The regression was statistically significant at the 0.01 level (F-test), with an R\({}^{2}\) of 0.92 and an MRE of 17.17% (see Figure 3). The atmospheric correction results for GOCI's sixth and eighth bands (ranging from 0.67 to 0.69 and 0.83 to 0.89, respectively) satisfy the inversion requirements, with relative errors of 12.31% and 2.93%. As the GOCI sixth and eighth bands correspond directly to the HJ CCD third and fourth bands, the selection of bands does not significantly affect the accuracy comparison between the two models.