Evapotranspiration in the Pampean Region using field measurements and satellite data |
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| Affiliation: | 1. Comisión de Investigaciones Científicas de Buenos Aires CIC, Instituto de Hidrología de Llanuras IHLLA/Pinto 399, 7000 Tandil, Provincia de Buenos Aires, Argentina;2. Agencia Nacional de Promoción Científica y Tecnológica de Argentina – ANPCyT/Pinto 399, 7000 Tandil, Provincia de Buenos Aires, Argentina;1. DICA, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, Italy;2. Department of Geoscience and Engineering, Delft University of Technology, Delft, The Netherlands;1. Department of Applied Physics, University of Valladolid, Valladolid, Spain;2. Department of Water Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands;1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China;4. Department of Computer Vision & Remote Sensing, Technische Universität Berlin, 10587 Berlin, Germany;5. Department of Geography, Ghent University, Ghent 9000, Belgium;6. Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium and Urumqi 830011, China;1. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;2. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China;3. School of Biological Sciences, The University of Hong Kong, Hong Kong, China;4. Department of Mathematics, The University of Hong Kong, Hong Kong, China |
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| Abstract: | Evapotranspiration (LE) is an important factor for monitoring crops, water requirements, and water consumption at local and regional scale. In this paper, we applied the semi-empirical model to estimate the daily latent heat flux (LEd = Rnd + A − B(Ts − Ta)). LEd has been estimated using satellite images (Thematic Mapper sensor) and a local dataset (incoming and outgoing short- and long-wave radiation) measured during three years. We first estimated the daily net Radiation (Rnd) from a linear equation derived from the instantaneous net Radiation (Rnd = CRni + D). Subsequently, coefficients A and B have been estimated for two different cover vegetations (pasture and soybean). For each vegetation cover, an error analysis combining Rnd, A, B, and surface and air temperatures has been calculated. Results showed that Rnd had good performance (nonbias and low RMSE). LEd errors for pasture and soybean were ±28 W m−2 and ±40 W m−2 respectively. |
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