## 3 Results and Discussion
### TSS Concentration Algorithm  
The TSS concentration retrieval algorithm was created utilizing the regression algorithm between the in situ TSS concentration and the in situ-measured remote sensing reflectance, Rrs (\(\lambda\)), based on single-band, two-band, and three-band proportion reflectance combinations. The in situ TSS concentration and in situ Rrs (\(\lambda\)) were utilized as the dependent and independent variables, respectively. Among the few combinations, the most elevated relationship between the two variables was shown by the most elevated coefficient of determination (R2), which was chosen as the retrieval algorithm. The regression algorithm for TSS concentrations is displayed in Table 3.  
As shown by Table 3, the highest R2 value for single-band regression obtained by SWIR (short-wave infrared) wavelength with the value is 0.1092. Meanwhile, the lowest was obtained by the blue band, with R2 = 0.0505. Meanwhile, the result of two band shows that the lowest R2 obtained by the ratio of SWIR/NIR bands with the value is 0.0001 and the highest value obtained by ratio green to red bands with the R2 value is 0.1131. The above result shows that the regression between TSS/Rrs(B6) in Teluk Lipat waters was strongly associated with Rrs(B3)/Rrs(B5), with the highest coefficients of determination R2 = 0.7900 with the standard error of estimate (SEE) being 119.804.  
Figure 3 shows the plot of TSS over band 6 against Rrs(B3)/Rrs(B5). The R2 and adjR2 for this plot are given by 0.79 and 0.77, respectively. As compared to the algorithm developed by [32], this result fully utilized the different sensitivities of the green, NIR and SWIR bands to TSS concentrations, as proven by many previous studies [25; 57]. Furthermore, the effectiveness of NIR wavelengths to assess TSSs is well documented [58]. Thus, these band combinations were selected for estimating TSS in Teluk Lipat. The equation is given by  
\[\text{TSS}=x_{1}\left[a\left(\frac{x_{2}}{x_{3}}\right)\ -\ b\right] \tag{4}\]  
where,  
\begin{table}
\begin{tabular}{c c c} \hline
**Band** & **Best Fit Equation** & **R2** \\ \hline Single & & \\ \hline B1 & TSS = \(-\)114.26 Rrs(B1) + 14.859 & 0.0607 \\ B2 & TSS = \(-\)91.935 Rrs(B2) + 15.012 & 0.0505 \\ B3 & TSS = \(-\)50.83 Rrs(B3) + 13.746 & 0.0414 \\ B4 & TSS = \(-\)88.187 Rrs(B4) + 13.782 & 0.0767 \\ B5 & TSS = \(-\)74.862 Rrs(B5) + 13.626 & 0.0815 \\ B6 & TSS = \(-\)124.15 Rrs(B6) + 14.25 & 0.1061 \\ B7 & TSS = \(-\)156.87 Rrs(B7) + 14.122 & 0.1092 \\ \hline Two band & & \\ \hline B6/B7 & TSS = 15.55 Rrs(B6/B7) \(-\) 9.1884 & 0.0605 \\ B6/B5 & TSS = 0.2873 Rrs(B6/B5) + 11.304 & 0.0001 \\ B3/B4 & TSS = 4.131 Rrs(B3/B4) + 4.3269 & 0.1131 \\ \hline Three band & & \\ \hline TSS/B1 vs. B2/B3 & TSS= Rrs (B1)[\(-\)2502.5 Rrs(B2/B3) + 2330] & 0.2819 \\ TSS/B6 vs. B4/B5 & TSS= Rrs(B4)[1426(B5/B6) \(-\) 745.37] & 0.3707 \\
**TSS/B6 vs. B3/B5** & **TSS = Rrs (B6)[564.68 Rrs(B3/B5) \(-\) 381.98]** & **0.7900** \\ TSS/B7 vs. B3/B4 & TSS = Rrs(B7)[566.47 Rrs(B3/B4) \(-\) 635.62 & 0.7619 \\ TSS/B6 vs. B3/B4 & TSS = Rrs(B3/B4) + 116.79] & 0.7570 \\ TSS/B6 vs. B3/B4 & TSS = Rrs(B6)[399.41 Rrs(B3/B4) \(-\) 439.7 & \\ TSS/B6 vs. B3/B4 & Rrs(B3/B4) + 97.191] & 0.7570 \\ \hline \end{tabular}
\end{table}
Table 3: Regression results of TSS against several Landsat 8 bands. B1-B7 represent the designated band of Landsat OLI. The wavelength for seven spectral Landsat bands (B1–B7) used in this study are 0.43–0.45, 0.45–0.51, 0.53–0.59, 0.64–0.67, 0.85–0.88, 1.57–1.65 and 2.11–2.29 \(\upmu\)m, respectively.  
\(x_{1}=\text{Rrs}(\text{B}_{6})\)  
\(x_{2}=\text{Rrs}(\text{B}_{3})\)  
\(x_{3}=\text{Rrs}(\text{B}_{5})\)  
\(a=564.68\), \(b=381.98\)