The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.
