## 1 Introduction
These two approaches are empirical or semiempirical and analytical or semi-analytical methods [16; 17]. Empirical or semi-empirical methods apply simple or multiple regression between measured water quality parameters and remote sensing reflectance of certain wavelengths to obtain estimated water quality parameters. The disadvantage of this method is that it is regional in nature and quite costly, particularly during field sampling procedure implementations. Meanwhile, the simplicity and rapidity of the data processing with sufficient accuracy are their main advantages [13; 18]. The second approach is analytical or semi-analytical. This method incorporates spectral optimization approaches and is based on solutions to the radiative transfer equations [18]. The inversion of the radiative transfer equations is mainly based on the neural network approach, utilizing a large database of radiative transfer simulations such as the Case-2 Regional/Coast Colour (C2 RCC) algorithm [19], and by using a windows-based program for modeling and analyzing optical in situ measurementsin aquatic environments, such as the Water Color Simulator (WASI) [20; 21]. In general, the advantages of this method are that the algorithms can be applied to the different water bodies, and the field sampling data are not required to develop the algorithm. However, the effectiveness of this kind of algorithm relies on the accuracy of the selected spectral models for the absorption coefficients of individual constituents that are present in the water; furthermore, this is a time-consuming process [18]. In general, C2 RCC only works well in optically deep water, whereas water constituents such as CDOM and sediment are neglected and WASI requires more experience and supervision in defining the initial values of the parameters [21]. With the intention being to regionally monitor TSS with low cost, simple and rapid data processing, and with a sufficiently accurate empirical method, this method will be utilized in this study. Theoretically, a single band could be used to develop the robust and sensitive TSS algorithm; if an appropriate band is chosen [3; 22; 23], successfully developed robust linear algorithm between TSS and red wavelength (0.62-0.67 um) of the MODIS Terra sensor have been demonstrated over the Gulf of Mexico. However, the developed algorithm is easy to saturate when the TSS increases significantly [24]. Due to the complexity of the water body, such as the color and size of the particles, the use of more than one wavelength is more practical [22; 23; 25] and less sensitive to backscatter variability [26]. Recently, many researchers have developed robust and sensitive TSS algorithms by utilizing several remote sensing bands ranging from blue to the near-infrared spectrum of lights over different regional areas. Since the late 1970s, the marvel of the total suspended sediment incrementally increasing with the brilliant vitality developing from the surface waters within the visible and near-infrared (NIR) districts of the electromagnetic range has been noted [27]. Many researchers are as of now fascinated by estimating the total suspended sediment concentration by utilizing remote sensing technology. To realize this objective, field and remote sensing information must be utilized. In common, researchers calibrate remote sensing information using field information to create an algorithm. Most researchers have proposed a relationship between reflected solar radiance measured by satellite-based rebellious and field-measured total suspended sediments inside a wide range of inland and coastal waters. In previous studies, total suspended sediment remote sensing models were used by previous researchers [28; 29; 30; 31]. However, the developed algorithms are highly regionally dependent. It is a drawback that the developed algorithm can only be applied over the study area and cannot be used in other regions.