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Evapotranspiration (ET) is a key component of the hydrological cycle, encompassing evaporation processes from soil and water surfaces and plant transpiration (Sun et al., 2017). Accurate estimation of ET is vital for effective water resource management, agricultural planning, and environmental monitoring (Gowda et al., 2008). However, the complex interactions between land surface conditions, vegetation, and atmospheric factors make direct measurement of ET challenging, leading to the development of various estimation methods. Remote sensing has become a widely used approach for estimating ET over large areas because it provides spatially comprehensive data (Xiao et al., 2024). Methods like the Surface Energy Balance Algorithm for Land and the Surface Energy Balance System utilise satellite-derived thermal imagery and meteorological inputs to calculate ET by analysing the energy exchanges between the land surface and the atmosphere. These methods are advantageous for their broad spatial coverage, making them particularly useful for regional to global scale studies. However, they require careful calibration and validation, and their accuracy can be affected by the spatial resolution of the satellite data and the quality of meteorological inputs. In addition to remote sensing, several other ET estimation methods are commonly employed.

This study investigates the cooling potential of vegetation in rural landscapes of the Czech Republic to mitigate heat-related issues. Using remote sensing, the Cooling Capacity Index (CCI) is assessed to measure green spaces' ability to lower air temperatures using evapotranspiration and shading. Landsat 8/9 and meteorological data are utilised, with CCI calculated based on vegetation cover, albedo, and evapotranspiration. Our results demonstrate significant variations in cooling capacity across different land use types. Forests exhibited the highest cooling potential, while urban areas, characterised by heat-absorbing materials, displayed the least. We analysed temporal and spatial variations in cooling capacity using various visualisation tools and validated the results against the InVEST software (v3.14.0). This study highlights the effectiveness of remote sensing in quantifying ecosystem functions, particularly the cooling services provided by vegetation. Our findings emphasise the crucial role of vegetation in mitigating urban heat islands and addressing climate change. This research provides valuable insights for developing climate change adaptation strategies in rural landscapes. [ABSTRACT FROM AUTHOR]

This study explores the Olomouc region's thermal dynamics and energy balance in northeastern Czech Republic during the spring and summer of 2022. Using Landsat 8 and Landsat 9 imagery, meteorological data and the S-SEBI model to determine Bowen Index (BI). Analysis of Solar Net Radiation and Sensible and Latent Heat Fluxes contributes to our understanding of energy exchanges at the surface. The S-SEBI algorithm helps determine Bowen Index, highlighting patterns across land cover classes. Urban and artificial areas consistently exhibit higher Bowen Index values, suggesting a higher sensible heat flux. Areas with vegetation showcase lower BI values, indicating the dominance of latent heat flux and the cooling effect of vegetation. Temporal trends in BI vary during specific sensing days. Despite challenges associated with atmospheric variability, our integration of meteorological data and remote sensing techniques contributes to understanding the local climate. Validation with EUMETSAT evaluation data indicates a consistent relationship with some biases, implying potential reliability of our approach and the derived heat fluxes and Bowen Index values. [ABSTRACT FROM AUTHOR]