Aim of the study:
The aim of this paper was to develop and test detection methods of sandbars for a selected section of the Lower Vistula river with the use of Sentinel-2 Level 2A optical images.

Material and methods:
The analyses were performed in QGIS (version 2.18.4) and SNAP (version 7.0) software. Both the image reprogramming and pixel value analysis were conducted in the SNAP. QGIS was used to perform activities involving the remainder of the work, such as steps with satellite data, i.e. processing, surface analysis, and their visualisation.In the case of multispectral images water indices were used, such as: NDWI (Normalized Difference Water Index); MNDWI (Modified Normalized Difference Water Index); AWEIsh (Automated Water Extraction Index shadow); AWEInsh (Automated Water Extraction Index no shadow); LSWI ( Land Surface Water Index); MLSWI (Modified Land Surface Water Index); MSI (Moisture Stress Index); SWM (Sentinel Water Mask) to separate sandbars from a water. These index methods base on a threshold value. Not all tested indices provided satisfactory results. Therefore, the layers were generated for 5 water indices.

Results and conclusions:
The analyses have shown that, for the selected section of the Lower Vistula, it is possible to detect sandbars in the river channel on the basis of Sentinel-2 satellite’s data. A proper selection of remote sensing index, creation of binary classification and selection of processing algorithm are important when detecting river islands. Every water index used had different results. The difference may be reduced by improving different threshold value algorithm.