## 1 Introduction  
Remote sensing offers substantial advantages over traditional monitoring methods, mainly because of the synoptic coverage and temporal consistency of the data, and it has the potential to provide crucial information on inland and near-coastal transitional waters [1]. For this reason, remote sensing is an essential tool for the study and monitoring of inland water bodies, often characterized by Case 2 waters (optically complex waters), especially when it comes to studying key variables to determine water quality along a reservoir's longitudinal profile. Because of this importance, many works develop algorithms based on in situ reflectance for various reasons: the validation of a previously published reflectance band-ratio algorithm [2] to use historical and unpublished data [3], obtaining algorithms in advance of the sensor's activity [3, 4], to develop cross-instrument algorithms to facilitate the spatial and temporal comparability of overlapping missions [5], and to achieve a wider range of data [6]. However, when the algorithms developed from in situ reflectances are applied to satellite imagery, their performance also depends on the errors in the water reflectance retrieval after the atmospheric correction. Estimating water quality variables from satellite images requires an accurate estimation of water-leaving radiance, but the radiation measured by the sensors has another important contributor, the atmosphere, consisting of atmospheric gases and aerosols, which account for at least 90% of the signal measured by the satellite sensor [7, 8]. Therefore, the atmospheric correction (AC) procedure is an important step to subtract the atmospheric contribution (scattering effects of aerosols, water vapor, etc.... )and sun glint (sunlight reflecting off the water surface at the same angle as the sensor) from the signal at the top of the atmosphere (TOA) [9]. With regard to Case 2 waters, the algorithms designed to perform the AC of land surfaces are not directly applicable to an aquatic surface because it is darker, and the air-water interface is not Lambertian [9]. Nevertheless, a significant correlation has been demonstrated between the reflectance obtained by the Sen2 Cor processor, developed for the land surface study of Sentinel-2 (S2-MSI) imagery, and the in situ reflectance for hypertrophic waters [10; 11]. In addition, Case 2 waters are much more complex than oceanic waters (Case 1 waters) because of their optically active constituents (OAC) as they contain a higher presence of phytoplankton, dissolved organic matter, and tripton, therefore requiring a specific AC. Each OAC has a very different effect on the reflective spectrum; the suspended matter increases the reflectance in the green, red, and NIR bands, while the CDOM (chromophoric dissolved organic matter) increases the absorption (reducing the reflectance) in the blue bands [12]. Consequently, they strongly condition the water spectrum because the percentage contribution of water reflectance in TOA varies depending on the predominance of suspended sediment or CDOM in the water [13]. Therefore, due to the higher complexity of inland waters in the NIR, the ACs designed for Case 1 waters are generally not applicable to Case 2 waters. This indicates that different ACs may be required for waters of different OAC compositions, depending on the part of the spectrum used to perform the atmospheric correction. To respond to this challenge, from before the launch of the S2-MSI mission satellites and up to the present day, different types of ACs are being developed for different Case 2 water types, even though the mission objective is soil and vegetation study. The great interest in S2-MSI is due to the fact that its usefulness has been proven for inland water study, thanks to the inclusion of new bands at the red edge (boundary of the red and infrared spectral regions), their radiometric quality, and their high spatial resolution [14].