Pasichnyk M., Hryhoriichuk V., Yushchenko V., Olenchuk R. Geospatial Identification of Channel Transformations in the Mouth Section of the Cheremosh River Using Sentinel-1 Radar Imagery
DOI: https://doi.org/10.17721/2306-5680.2026.1.1
Hydrology, Hydrochemistry and Hydroecology. 2026. № 1 (79)
Publication language: Ukrainian
Authors:
Pasichnyk M., Yuriy Fedkovych Chernivtsi National UniversityHryhoriichuk V., Yuriy Fedkovych Chernivtsi National UniversityYushchenko V., Yuriy Fedkovych Chernivtsi National UniversityOlenchuk R., Yuriy Fedkovych Chernivtsi National University
The presented article scientifically substantiates a comprehensive methodology for the geospatial identification and retrospective monitoring of channel transformations in the estuary section of the Cheremosh River for the period from 2015 to 2025. The relevance of the study is driven by the high dynamics of foothill rivers in the Carpathian region, where the channels are composed of easily erodible alluvial deposits, and the change in the hydrological regime under the influence of global warming provokes a rapid intensification of lateral erosion. The traditional use of multispectral optical satellite imagery for this region is significantly limited by the high recurrence of dense cloud cover precisely during flood events, when the most large-scale channel reformations occur. Therefore, the microwave radar sensing of the Sentinel-1 mission was unequivocally chosen as the primary observation tool, ensuring all-weather and round-the-clock monitoring of the channel geometry.
The methodological basis of the work relies on the physical principles of the interaction of C-band electromagnetic radiation with the water surface, which acts as a specular reflector. To precisely detect the water mirror and mitigate the influence of complex background vegetation, the Sentinel-1 Dual-Polarized Water Index (SDWI) was calculated within the QGIS geoinformation system environment. An important technological innovation of the study is the use of input data exclusively in the linear scale of backscattering intensity for the VV and VH channels, which guarantees strict mathematical correctness of the calculations.
The next key stage of the spatial analysis was the binarization of the obtained raster images using the self-adaptive Otsu’s algorithm. This approach made it possible to automatically determine the optimal segmentation threshold and maximally objectively separate water bodies from wet intra-channel gravel bars. After conversion to a vector format, the resulting initial polygonal models underwent necessary procedures of spatial filtering, buffering, and morphological smoothing, which ultimately ensured the hydromorphological correctness of the contours.
The results of the vector analysis of the series of created masks clearly confirmed the significant spatial dynamics of the river network. A particularly pronounced tendency towards lateral channel migration and an intensification of lateral erosion were detected at control sites during the inter-monitoring period of 2020–2025. The comparative analysis proved that the configuration and continuity of the identified water mirror are a direct function of the hydrological phase: from a state of deep low water with fragmented branches in 2015 to the high-water periods of subsequent years. The proposed reproducible automated GIS approach minimizes the subjectivity of expert assessments and forms a reliable analytical basis for predicting hydroecological risks and developing effective strategies for the sustainable management of fluvial systems.
Keywords: Radar sensing, channel transformations, Sentinel-1, SDWI, QGIS.
References:
1. Copernicus. Comparison of Sentinel-2 and Landsat Data for Surface Water Mapping. Available at: Copernicus. Comparison of Sentinel-2 and Landsat Data for Surface Water Mapping, 2022. URL: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2-comparison-sentinel-2-and-landsat
2. Copernicus Land Monitoring Service. Products including DEM. URL: https://land.copernicus.eu/
3. Cortes V., Jaramillo Pineda J. A., Echeverri C., Gonzáles C., Bolaños S., Olmos C., Díaz J. Sentinel-1 SAR imagery (VV and VH polarization) for hydraulic dynamics analysis in the Bajo Cauca region, Antioquia–Colombia. Mendeley Data. 2025. V1. URL: https://doi.org/10.17632/h6p2c3ffkd.1.
4. Henderson R. E., Farris A. S., Kratzmann M. G. et al. Digital Shoreline Analysis System version 6.1: U.S. Geological Survey software release. U.S. Geological Survey, 2026. URL: 10.5066/P1NHMJNC.
5. Himmelstoss E. A., Henderson R. E., Kratzmann M. G., Farris A. S. Digital Shoreline Analysis System (DSAS) version 5.0 user guide. U.S. Geological Survey Open-File Report 2018–1179. 2018. P. 1-110. URL: https://doi.org/10.3133/ofr20181179.
6. Jia P., Yang Z., Wang J., Zhang G. Sentinel-1 derived water index (SDWI) for large-scale water body identification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019. Vol. 12, № 6. P. 1–15. URL: https://doi.org/10.1109/JSTARS.2019.2897861.
7. Kim J. C., Choi Y. J., Yoon Y. J. Application Research of Sentinel-1 SAR in Flood Range Extraction and Polarization Analysis. Journal of Geo-information Science. 2021. Vol. 23, № 6. P. 1063–1070. URL: https://doi.org/10.12082/dqxxkx.2021.200717.
8. Langat P. K., Kumar L., Koech R. K. Monitoring river channel dynamics using remote sensing and GIS techniques. Geomorphology. 2019. Vol. 325. P. 92-102. URL: https://doi.org/10.1016/j.geomorph.2018.10.007.
9. Li X. et al. Flood water extraction in the hilly Poyang Lake region using Sentinel-1 SAR data and CAU-Net. Remote Sensing. 2023. Vol. 15, № 21. P. 5247. URL: https://doi.org/10.3390/rs15215247.
10. Nasir A. et al. Enhancing Flood Detection in Surabaya: A Comparative Study of VV and VH Polarizations with Sentinel-1 Data. Journal of Civil Engineering. 2023. Vol. 40, № 1. P. 89-102. URL: https://doi.org/10.17632/h6p2c3ffkd.1.
11. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979. Vol. 9, № 1. P. 62-66. URL: https://doi.org/10.1109/TSMC.1979.4310076.
12. Pasichnyk M., Buzey O. Inventory and functional classification of ponds based on Sentinel-2 NDWI data in the Sovytsia Kitsmanska River basin (Ukraine). Naukovyi visnyk Chernivetskoho universytetu: Heohrafiia. 2025. № 854. S. 150–169. URL: https://doi.org/10.31861/geo.2025.854.150-169.
13. Pasichnyk M., Yushchenko Y., Palanychko O. et al. Remote sensing and GIS in the research of young river landscape. Grassroots Journal of Natural Resources. 2025. Vol. 8, № 1. P. 163–189. URL: https://doi.org/10.33002/nr2581.6853.080106.
14. Calculating raster area (QGIS3). QGIS Tutorials. 2023. URL: https://www.qgistutorials.com/en/docs/3/calculating_raster_area.html
15. Rapinel S., Fabre E., Dufour S. et al. Mapping potential, existing and efficient wetlands using free remote sensing data. Journal of Environmental Management. 2019. Vol. 247. P. 829–839. URL: https://doi.org/10.1016/j.jenvman.2019.06.098.
16. Rizzoli P., Martone M., Gonzalez C. et al. Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing. 2017. Vol. 132. P. 119–139. URL: https://doi.org/10.1016/j.isprsjprs.2017.08.008.
17. Silva Júnior J., Silva Júnior J. Water Body Extraction using Artificial Neural Networks and Sentinel-1. Sociedade & Natureza. 2024. Vol. 36. e70654. URL: https://doi.org/10.14393/SN-v36-2024-70654.
18. Tan J., Tang Y., Liu B. et al. A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block // Remote Sensing. 2023. Vol. 15, № 10. P. 2690. URL: https://doi.org/10.3390/rs15102690.
19. Yushchenko Yu. S., Pasichnyk M. D., Darchuk K. V. et al. Contemporary geoinformation technologies in postmodern education of geographers, hydrometeorologists, land surveyors. Postmodern Openings. 2022. Vol. 13, № 2. P. 409-429. URL: https://doi.org/10.18662/po/13.2/462.
20. Zhai M. et al. Water Body Extraction Methods for SAR Images Fusing Sentinel-1 Dual-Polarized Water Index and Random Forest. Sensors (Basel). 2025. Vol. 25, № 15. P. 4868. URL: https://doi.org/10.3390/s25154868.
21. Zitti G. et al. A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm. Remote Sensing. 2023. Vol. 15, № 10. P. 2690. URL: https://doi.org/10.3390/rs15102690.
HOW TO CITE
Pasichnyk, M., Hryhoriichuk, V., Yushchenko, V., Olenchuk, R. (2026). Geospatial Identification of Channel Transformations in the Mouth Section of the Cheremosh River Using Sentinel-1 Radar Imagery. Hidrolohiia, hidrokhimiia i hidroekolohiia [Hydrology, Hydrochemistry and Hydroecology], 1(79), 6-18 (in Ukrainian, abstr. in English). https://doi.org/10.17721/2306-5680.2026.1.1