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Mapping watercourses and their surroundings through remote sensing methods is a fast, continuous, and effective method and is a crucial tool for capturing change and possibly predicting hazards. Thanks to Synthetic Aperture Radar (SAR) technology and the ability of its backscattered and emitted radiation to penetrate the atmosphere under any conditions, this type of mapping of water surfaces is of particular importance. This paper presents the possibility of using SAR technology for long-term observations of changes in the behaviour of rivers and river systems, combined with optical multispectral images Sentinel-2. Additionally, it aims to demonstrate the suitability of satellite SAR and multispectral data implementation for mapping changes in watercourses, caused not only by their natural development but especially by inundation processes in their catchment area. Appropriate Sentinel-1 image processing evaluation procedures demonstrate that the usage of vertical-vertical (VV) type polarisation configuration is a suitable methodology for documenting water bodies, and a Lee filter is an acceptable tool for radar noise filtering. The extraction process of water surfaces is based on the determination of threshold values using the “Otsu” principle. Subsequently, the comparison of the results is realised by the spectral indices of water—the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and supervised classification method Maximum Likelihood Classification (MLC). The results are numerical and graphical evaluated. In assessing the accuracy of SAR extraction, the highest values achieved in Overall Accuracy (OA) were a maximum of 98.6%. On average, the lower values were in User Accuracy (UA) with a maximum of 93.1%, where VV polarisation also dominates. However, vertical-horizontal (VH) polarisation dominates in Producer Accuracy (PA) with a maximum of 84.9%.

Assessment of the Usability of SAR and Optical Satellite Data for Monitoring Spatio-Temporal Changes in Surface Water: Bodrog River Case Study