Journal of Radio Electronics. eISSN 1684-1719. 2026. №1
Full text in Russian (pdf)
DOI: https://doi.org/10.30898/1684-1719.2026.1.3
Hybrid satellite monitoring and AI platform
for rapid identification of flood conditions
E.V. Savchenko, S.M. Maklakov
Kotelnikov Institute of Radioengineering and Electronics of RAS, Fryazino Branch,
Vvedenskogo sq., 1, Fryazino, Moscow region, Russia
The paper was received December 19, 2025.
Abstract. A methodology for constructing a hybrid operational satellite monitoring platform for the early detection of flood zones is presented. The approach combines optical (Sentinel-2), radar (Sentinel-1), and geostationary (SEVIRI) observations, precipitation estimation products (MPE), and ground-based verification. Unified data preprocessing is performed, including georeferencing, reduction to a single spatial resolution, cloud filtering, and windowing of large scenes. A set of index indicators (NDWI, MNDWI, AWEI, SWI, etc.), texture characteristics, and radar features are calculated. The results are integrated by a neural network module, which generates probabilistic flood maps and confidence indicators for each selected polygon. The implementation is performed in the Python environment using standard libraries for working with raster data and training neural networks. At the same time, modularity is emphasized: individual stages (downloading and saving scenes, preprocessing, generating a training set, model training, and postprocessing) can be performed independently and reused for other events and regions. Testing on flood data in Crimea (June 2021) demonstrated improved detection accuracy and robustness compared to standard index-based methods and confirms the practical applicability of the proposed solution for operational monitoring and alerting, provided the labeled set is further expanded and local landscape features are taken into account.
Key words: floods, climate research, data visualization, remote sensing, satellite sensing, artificial intelligence.
Financing: The work was carried out within the framework of the state assignment of the V.A. Kotelnikov Institute of Radioelectronics of the Russian Academy of Sciences No. 075-00395-25-00.
Corresponding author: Maklakov Sergey Michailovich, ser2110@mail.ru
References
1. Mitnik L.M., Kuleshov V.P., Mitnik M.L., Pichugin M.K., Khazanova E.S. Satellite monitoring of flooding in Primorye in 2017 // Collection of abstracts of the fifteenth All-Russian Open Conference "Modern problems of remote sensing of the Earth from space", Moscow, November 13-17, 2017 / Space Research Institute of the Russian Academy of Sciences. Moscow: Space Research Institute of the Russian Academy of Sciences, 2017, p. 102. (In Russian)
2. Notti D. et al. Potential and limitations of open satellite data for flood mapping // Remote sensing. – 2018. – Т. 10. – №. 11. – С. 1673.
3. Schmetz J. et al. An introduction to Meteosat second generation (MSG) // Bulletin of the American Meteorological Society. – 2002. – Т. 83. – №. 7. – С. 977-992. https://doi.org/10.1175/1520-0477(2002)083%3C0977:AITMSG%3E2.3.CO;2
4. Schmetz J. et al. Monitoring weather and climate with the Meteosat and Metop satellites // Revista de Teledetección. – 2007. – Т. 27. – С. 5-16. http://www.aet.org.es/revistas/revista27/AET27-01.pdf
5. MSG Meteorological Products Extraction Facility Algorithm Specification Document 23 October 2015, v7B e-signed // Eumetsat.int. URL: https://www.eumetsat.int/media/38993
6. Nerushev A.F., Visheratin K.N., Ivangorodskiy R.V. Characteristics of the wind field in the upper troposphere as indicators of climate variability // Earth Exploration from Space, 2023, No. 4, pp. 92-106. https://doi.org/10.31857/S0205961423030053 (In Russian)
7. The main instruments carried by the MSG satellites are the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Geostationary Earth Radiation Budget (GERB) // Eumetsat.int. URL: https://www.eumetsat.int/meteosat-second-gen-instruments
8. Gerardo R., de Lima I.P. Comparing the Capability of Sentinel-2 and Landsat 9 Imagery for Mapping Water and Sandbars in the River Bed of the Lower Tagus River (Portugal) // Remote Sens. 2023, 15, 1927 // https://doi.org/10.3390/rs15071927
9. Shen G., Fu W., Guo H., Liao J. Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake. Water 2022, 14, 1902. // [Электронный ресурс]. URL: https://doi.org/10.3390/w14121902
10. Gulácsi A., Kovács F. Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine // Remote Sens. 2020, 12, 1614. https://doi.org/10.3390/rs12101614
11. Kumah K.K. Near-real-time rainfall detection and estimation from commercial microwave links and Meteosat Second Generation data // University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2022. 215 p.
12. Meirink J.F., Roebeling R.A., and Stammes P. Inter-calibration of polar imager solar channels using SEVIRI // Atmos. Meas. Tech., 6, 2495–2508 https://doi.org/10.5194/amt-6-2495-2013
13. Doxani G. et al. Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land // Remote Sensing of Environment. – 2023. – Т. 285. – С. 113412. https://doi.org/10.1016/j.rse.2022.113412
14. McFEETERS S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features // International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
15. Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery // International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
16. Feyisa G.L. et al. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery // Remote sensing of environment. – 2014. – Т. 140. – С. 23-35.
17. Sharma R.C., Tateishi R., Hara K., Nguyen L.V. Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data // Remote Sens. 2015, 7, 13807-13841. https://doi.org/10.3390/rs71013807
18. Liu S., Wu Y., Zhang G., Lin N., Liu Z. Comparing Water Indices for Landsat Data for Automated Surface Water Body Extraction under Complex Ground Background: A Case Study in Jilin Province. Remote Sens, 2023, 15, 1678.
19. Mastering NDWI: Understanding Water Thresholds and Ranges for Precision Agriculture // [Электронный ресурс]. URL: https://farmonaut.com/remote-sensing/mastering-ndwi-understanding-water-thresholds-and-ranges-for-precision-agriculture (In Russian)
20. Anwer Hossam Aldeen & Mohamed Tarig. Prediction of Flood Monitoring for Satellite Image Using Artificial Neural Networks // ResearchGate. 2025. https://www.researchgate.net/publication/387599533_Prediction_of_Flood_Monitoring_for_Satellite_Image_Using_Artificial_Neural_Networks
21. Hernandez L., Ali M., Zhang S. Geospatial Artificial Intelligence for Satellite-based Flood Extent Mapping: Concepts, Advances, and Future Perspectives. // ResearchGate. 2025. https://arxiv.org/html/2504.02214v2
22. Wu Xuan, Zhang Zhijie, Xiong Shengqing, Zhang Wanchang, Tang Jiakui, Li Zhenghao, An Bangsheng, Li Rui. A Near-Real-Time Flood Detection Method Based on Deep Learning and SAR Images // Remote Sensing, 15(8), 2046, 2023. URL: https://www.mdpi.com/2072-4292/15/8/2046
23. Pranath Reddy Kumbam, Kshitij Maruti Vejre. FloodLense: A Framework for ChatGPT-based Real-time Flood Detection // arXiv preprint, arXiv:2401.15501v1, 2024. https://arxiv.org/html/2401.15501v1
24. Storm warning about dangerous hydrometeorological phenomena in the Republic of Crimea for June 16-17, 2021. // [web]. URL: https://82.mchs.gov.ru/deyatelnost/press-centr/vse_novosti/4489285 (In Russian)
25. Storm warning about dangerous hydrometeorological phenomena in the Republic of Crimea for June 21-22, 2021. // [web]. URL: https://82.mchs.gov.ru/deyatelnost/press-centr/vse_novosti/4493462 (In Russian)
26. The Main Directorate of the Russian Ministry of Emergency Situations in the Republic of Crimea. Official website, press center section, operational information // [web]. URL: https://82.mchs.gov.ru/deyatelnost/press-centr/operativnaya-informaciya (In Russian)
27. The observation network of meteorological stations of the Republic of Crimea. // Roshydromet. FGBU "Crimean UGMS" [web]. URL: https://meteo.crimea.ru/?page_id=99 (In Russian)
28. Diederik P. Kingma, Jimmy Ba. Adam: A Method for Stochastic Optimization // Arxiv. https://doi.org/10.48550/arXiv.1412.6980
For citation:
Savchenko E.V., Maklakov S.M. Hybrid satellite monitoring and AI platform for rapid identification of flood conditions // Journal of Radio Electronics. – 2026. – №. 1. https://doi.org/10.30898/1684-1719.2026.1.3 (In Russian)