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Remote sensing technology and its terrestrial components are used for detecting environmental problems. One of the most important environmental problems in developing and developed countries is water pollution [1]. Water quality that was affected by water pollution is an important environmental factor [2]. The environmental factors affect people and their economic activities as well as concentrations of different water components, such as a waterbody, colored dissolved organic matter, chlorophyll, and total suspended solids. The water components can be measured and detected using satellite-based remote sensing [3, 4]. Although there are 16 river basins with a water popebtial of over 5 billion m 3 , such as Marmara,

ABSTRACT:Water resources are getting more and more important with each passing day in case of survival of humanity. For this reason, assessing water resources' quality and also monitoring them have attracted lots of attention in the recent years. Remote sensing has been growing widely in the last decade and its resources are very usable when it comes to water resources management. In this study, by using remote sensing technology, satellite images that have 350 to 1050 nanometres wavelength band sensors (e.g. CHRIS Proba) are used to determine the quality of the Kizilirmak River's water. Kizilirmak River is born and also pours out to sea in country limits of Turkey. It is the longest river of the country by the length of 1355 kilometres. Through the river's resources, ground based spectral measurements are made to identify the quality differences of the water at the test spots that have been determined before. In this context at Imranli, where the river contacts civilization for the first time, which is located in Sivas city of Turkey, samples are gathered in order to do ground based spectroradiometer measurements. These samples are gathered simultaneously with the image acquiring time of CHRIS Proba satellite. Spectral signatures that are obtained from ground measurements are used as reference data in order to classify CHRIS Proba satellite's hyperspectral images over the study area. Satellite images are classified based on Chemical Oxygen Demand (COD), Turbidity and Electrical Conductivity (EC) attributes. As a result, interpretations obtained from classified CHRIS Proba satellite hyperspectral images of the study area are presented.