Land Cover Modelling with Sentinel 2 in Water Balance Calculations of Urban Sites
DOI:
https://doi.org/10.33038/jcegi.4520Keywords:
Urban hydrology, Sentinel 2, Machine learning algorithm, Evapotranspiration, Runoff, InfiltrationAbstract
This study investigates how a land cover map is produced using Sentinel 2 satellite images and how the resulting map is integrated with the calculation of hydrological parameters. Due to its capacity to deliver high-resolution spatial data, satellite imagery is increasingly being used for land cover mapping (1). The process involves several stages, including pre-processing of the Sentinel 2 imagery, image classification using machine learning algorithms, and post-processing of the classified image to generate a land cover map. The resulting land cover map is then integrated with hydrological parameters such as precipitation, evapotranspiration, and soil characteristics to calculate various hydrological parameters. Debrecen, north-east Hungary is the study area. This study's method is suitable for usage in other cities. Understanding the relationship between land cover and water availability requires the integration of the land cover map with hydrological parameters (2). The study demonstrates the potential of using remote sensing data for hydrological studies and highlights the importance of integrating various data sources for accurate estimation of hydrological parameters. The results show that the land cover classes have a significant impact on the water balance of urban sites. This study outlines the key steps involved in creating a land cover map using Sentinel 2 satellite imagery and integrating it with hydrological parameters calculation. The application of this strategy supports sustainable water management techniques and offers insightful information about a region's hydrological processes.
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