Abstract
Evapotranspiration (ET) is one of the least understood components of the hydrological cycle. Its applications are varied, from agricultural, ecological and hydrological monitoring, to control of the evolution of climate change. The goal of this work was to analyze the influence that uncertainties in the estimate of land surface temperature (Ts) can cause on ET estimates by S-SEBI model in the Pampa biome area. Also, the specificities of native grassland of Pampa biome related to energy balance were analyzed. The results indicate that the daily evapotranspiration is higher when the pixel Ts is lower, which also shows the influence of land use on the variability of ET. The results demonstrated that the S-SEBI is less dependent on Ts estimation than other models reported in the literature, such as the SEBS, which not exceed 0.5 mm/day in grasslands. The evapotranspiration variability between forest and grassland were lower than expected, demonstrating that the Pampa biome have in Rio Grande do Sul the same importance that forests regarding to the processes of the hydrological cycle, since it covers 63% of the State.
Highlights
The physical, chemical and biological processes responsible for life on Earth depend practically on solar energy
The main objective of this work was to analyse the influence that the uncertainties in the estimate of Ts can cause on daily ET estimates by Simplified Surface Energy Balance Index (S-SEBI) model, we evaluated the model with eddy-covariance measurements in a native grassland area in Southern Brazil
The energy balance components estimated with S-SEBI and in situ measurements are shown in Figure 2 considering all scenes available
Summary
The physical, chemical and biological processes responsible for life on Earth depend practically on solar energy. Monitoring energy and soil-vegetation-atmosphere mass transfers is a key step in the management of water and agricultural resources, with different applications depending on the spatial and temporal scales of interest. It is useful for a better understanding and prediction of climate evolution [1,2]. Atmosphere 2020, 11, 1059 represents the energy available by the system to non-radiative process, such as evaporate of water or evapotranspiration on vegetated surfaces, by the latent heat flux (LE), heat the atmosphere by the sensible heat flux (H) and heat the subsoil by soil heat flux (G) [1,4,5]. The identification of the uncertainties resulting from the different input variables in the estimation of ET remains a challenge due to the complexity of the parameterization of the models [9,10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
