In this study, a remote sensing-based method of mapping and predicting fish spatial distribution in inland waters is developed. A combination of Earth Observation data, in-situ measurements, and hydroacoustics is used to relate fish biomass distribution and water-quality parameters along the longitudinal transect of the Římov Reservoir (Czech Republic) using statistical and machine learning techniques. Parameter variations and biomass distribution are estimated and validated, and apparent trends are explored and discussed, together with potential limitations and weaknesses. Water-quality parameters exhibit longitudinal gradients along the reservoir, while calculations reveal a distinct fish assemblage pattern observed as a patchy overall biomass distribution. Although the proposed methodology has a great potential for sustainable water management, careful planning is needed to ensure the simultaneous acquisition of remote sensing and in-situ data to maximize calibration accuracy.
