ANALYSIS OF METHODS FOR SOIL MOISTURE WATER BALANCE USING SATELLITE DATA
DOI:
https://doi.org/10.34185/1991-7848.itmm.2026.01.095Keywords:
Water balance, soil moisture, remote sensing, machine learningAbstract
This paper summarises approaches to assessing the water balance and soil moisture based on Earth remote sensing data and analyses their adaptation to conditions in Ukraine. It demonstrates that, as a result of climate change and the deterioration of water infrastructure, the water balance is reaching a critical state, whilst traditional observations are spatially limited. Optical, thermal and microwave methods are considered, as well as downscaling approaches for integrating satellite data of varying resolutions. The feasibility of a multi-method approach combining NDVI/NDWI, LST, SAR (Sentinel-1), SMAP and ERA5-Land to obtain spatially continuous estimates is justified. The potential of Google Earth Engine for processing time series and automating monitoring is noted. The prospects for applying machine learning methods to water balance mapping are identified.
References
World Bank. Water Sector Damage Assessment. Available online: https://eu4waterdata.eu (accessed: 2026).
Freitas, F.R.; et al. Review of soil moisture estimation methods using remote sensing. Advances in Technology, 2025. https://doi.org/10.1016/j.atech.2025.100648.
Bauer-Marschallinger, B.; et al. Soil moisture retrieval from Sentinel-1 data. Remote Sensing of Environment, 2025. https://doi.org/10.1016/j.rse.2025.114771.
Semenova, I.; et al. Dovhostrokova minlyvist ta tendentsii meteorolohichnykh posukh v Ukraini [Long-term variability and trends of meteorological droughts in Ukraine]. International Journal of Climatology, 2024. https://doi.org/10.1002/joc.8416.
Zarepour, M.; et al. Extreme droughts under climate change. Climatic Change, 2025. https://doi.org/10.1007/s10584-025-03958-9.
Hapich, H.; Novytskyi, R.; et al. Naslidky rosiisko-ukrainskoi viiny dlia vodnoi bezpeky ta povoienni perspektyvy [Impacts of the Russia–Ukraine war on water security and post-war prospects]. Natural Hazards Research, 2024. https://doi.org/10.1016/j.nhres.2024.01.004.
UNICEF; World Bank. Third Rapid Damage and Needs Assessment (RDNA3). Available online: https://documents.worldbank.org (accessed: 2026).
Hapich, H.; et al. Vplyv viiny na pryrodno-klimatychnu transformatsiiu terytorii u zoni zroshennia [Impact of war on natural-climatic transformation in irrigated areas]. SN Applied Sciences, 2025. https://doi.org/10.1007/s42452-025-07404-4.
Chun, J.; et al. Soil moisture estimation based on Sentinel-1 SAR using artificial neural networks. Remote Sensing, 2022, 14. https://doi.org/10.3390/rs14030465.
He, L.; et al. Soil moisture retrieval by integrating Sentinel-1A and MODIS data. Water, 2020, 12. https://doi.org/10.3390/w12061726.
Gruber, A.; et al. Evolution of soil moisture climate data records in ESA CCI. Earth System Science Data, 2019, 11, 717–739. https://doi.org/10.5194/essd-11-717-2019.
Guo, Y.; et al. Microwave-optical multi-stage synergy for downscaling soil moisture to 30 m. Remote Sensing, 2025, 17. https://doi.org/10.3390/rs17223677.
Razdavovskyi, R.; et al. Otsinka anomalii vrozhainosti silskohospodarskykh kultur v Ukraini na osnovi danykh Copernicus Sentinel ta ERA5 [Crop yield anomaly assessment in Ukraine based on Copernicus Sentinel and ERA5 data]. Sensors, 2024, 24. https://doi.org/10.3390/s24072257.
Snizhko, S.; et al. Richka Pivdennyi Buh: vodna bezpeka ta klimatychni zminy [Southern Bug River: water security and climate change]. Frontiers in Water, 2024. https://doi.org/10.3389/frwa.2024.1447378.
