Remote sensing-based water body extraction is essential for monitoring hydrological dynamics, particularly in reservoirs with pronounced seasonal variability. This study evaluates automated surface water identification using multi-sensor satellite data, focusing on validation against hydrological observations. The workflow was implemented in the Google Earth Engine environment using Sentinel-2 multispectral imagery acquired between 2018 and 2023 and filtered for cloud cover below 20%. Water extent was extracted using commonly applied spectral indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), and Water Ratio Index (WRI), and compared with water level records from the Veľká Domaša reservoir. The results show strong agreement between extracted water extent and water levels, with Spearman correlation coefficients ranging from 0.92 to 0.96 for all indices except AWEInsh, which exhibited higher variability likely due to sediment and vegetation influences. Maximum and minimum water extents (12.58 km2 and 9.04 km2) were consistent with observed hydrological trends. Validation using Sentinel-1 SAR data achieved an average Overall Accuracy of 98.6%, with VH polarization outperforming VV. Comparison with high-resolution aerial orthophotos revealed surface area differences of 0.20–1.26%. Automated thresholding produced results comparable to manual delineation, with minor and consistent deviations, confirming its reliability for repeatable water body extraction. Overall, the study demonstrates the effectiveness of spectral indices and automated approaches for long-term reservoir monitoring.
Kseňak et al. (2026) studied this question.