Field and airborne hyperspectral measurements supported the potential of the planned HyspIRI mission in the differentiation of pelagic _Sargassum_ from other floating materials [45]. The studies based on spaceborne hyperspectral images are mainly limited to sparse studies based on the Hyperion mission. A physics-based retrieval of Chl-a and TSM concentrations showed promising results for the Lake Garda (Italy), whereas the retrieval of CDOM was not feasible due to its low concentration and accordingly low SNR in Hyperion data [46]. Hyperion images were used also for mapping bathymetry in a complex coastal environment in Florida [47]. The Hyperion data provided improvements in mapping seagrass abundance in coastal areas in Florida compared to the results obtained from Landsat 5 Thematic Mapper (TM) and Advanced Land Imager (ALI) [48]. However, the low SNR and radiometric instability are addressed in several studies as a limiting factor in retrieving in-water constituents [36; 46; 49]. Although the wealth of spectral information provided by spaceborne hyperspectral images offers opportunities for in-depth studies of aquatic systems, the relatively low SNR of the past missions implies a key challenge [32; 47]. The nominal SNR of PRISMA is \(>\)200:1 for visible near-infrared (VNIR) bands [50] that can potentially provide high-quality data for aquatic applications. The SNR is critical for aquatic applications as the downwelling irradiance is subject to strong attenuation in the water column and the reflected signal is normally restricted to only 10-20%. In this context, atmospheric and sun-glint artifacts can contaminate the observed signal to a large extent [28]. In this study, we retrieve the water quality parameters from PRISMA level 2D images (i.e., surface reflectance products) based on a fully physics-based approach. Physics-based methods account for the interaction of the light with different media/layers between the sensor and the water column to the depth that the signal can penetrate [51; 52]. These types of methods approximate the absorption and backscattering characteristics of the atmosphere (aerosols, water vapor, etc. ), water surface, pure water, and in-water constituents through a radiative transfer model [53]. Other methods are based on training a regression model between image-derived spectral features and associated in-situ measurements of the biophysical parameter of interest [7; 54; 55; 56; 57]. A key advantage of physics-based methods is that in-situ measurements are not required to perform the retrieval. However, the physics-based inversion of in-water constituents requires precise atmospheric correction which is not critical for regression-based approaches [7; 28]. Moreover, knowledge about bio-optical properties such as phytoplankton species and the variation range of the constituents is required for the parametrization of physics-based models. Here, we rely on a physics-based approach rather than a regression-based one to neglect the dependency on the in-situ data. A very recent study examined the quality of PRISMA level 1 imagery, i.e., top-of-atmosphere (TOA) data, for aquatic applications [58]. The study demonstrated high consistency of TOA radiance observed by PRISMA and Sentinel-2 MSI sensors suggesting their synergic use for aquatic applications though more investigation is recommended [58]. In this study, we examine the potential of PRISMA level 2D imagery in deriving water quality parameters considering the case of a turbid lake. Here, we adapt the radiative transfer model implemented in the Water Color Simulator (WASI) [59] for processing the PRISMA level 2D products. The WASI processor is publicly available, sensor-independent, and flexible in adapting to different bio-optical conditions [59; 60; 28; 61]. The primary goal of this study is to assess the consistency of the PRISMA-derived \(R_{rs}\) and water quality products with those of synthesized and real Sentinel-2 imagery.