Water bodies are among most sensitive ecological environments. In order to ensure good water quality and establish framework for their protection European Parliament was adopted the Water Framework Directive (WFD) (Directive 2000/60/EC). The biological, hydro morphological and physic chemical quality parameters which are relevant for assessment of ecological status of water body are defined in Annex V of WFD. Traditionally, quality of surface water bodies are monitored by in situ measurements resulting in low spatial and temporal resolution of historical data. Remote sensing has great potential for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. In order to provide reliable monitoring of water quality, surface reflection derived by multispectral sensors need to be integrated with in situ measurements. Relationship between remote sensing and in situ data is usually modeled by using empirical, machine learning or deep learning algorithms. In this study, a 4-year (2013-2016) result of in situ monitoring of surface water bodies in Serbia are used for calibration and validation of algorithm for water quality monitoring based on Landsat 8 satellite image. The Turbidity, Suspending Sediments, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia are monitored. The Neuron Networks and Supported Vector Machine are used to analyzing correlation between in situ measurements and Landsat 8 atmospherically corrected satellite images. Feature more, capabilities of Landsat 8 are compared with Sentinel 2 images (2-years, 2015-2016). In situ data are provided by Agency for environment protection of Serbia.