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23 October 2012 Basin-scale evapotranspiration assessment based on vegetation coefficients derived from thermal remote sensing
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Evapotranspiration (ET) is a critical variable in hydrological processes and an accurate estimation of the rate of evapotranspiration is required if we wish to apply integrated management procedures to water resources. This study offers new insights into remote sensing-based models that estimate ET at basin scale, evaluating the combination of a surface energy balance based on thermal remote sensing and the use of the crop coefficient (Kc), a simple operational method that is widely used in irrigated agriculture. The study area is the Guadalfeo river basin in southern Spain, a large watershed with major topographical and landscape contrasts. Reference evapotranspiration (ETo) surfaces were generated by applying the FAO56-PM [1] equation, and real ET surfaces were estimated following a two-source energy balance model [2] [3]. Crop and vegetation coefficients were obtained as the ratio between ET and ETo. Kc maps were analysed in terms of vegetation type and development. The resulting coefficients generally ranged between 0.1 and 1.5, and could be directly related to vegetation ground cover for the main vegetation types, including natural vegetation and crops, with the determination coefficient (r2) lying between 0.77 and 0.97 in both humid and dry seasons. Relationships based on these coefficients are proposed as a simple proxy to monitor the water use of the basin on a regular basis by means of optical remote sensors alone, providing data with higher frequency and spatial resolution than can be obtained by thermal measurements; data that could complement thermal sensors whenever these were available.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Andreu, C. Aguilar, M. J. Polo, Elisabeth Carpintero, and M. P. González-Dugo "Basin-scale evapotranspiration assessment based on vegetation coefficients derived from thermal remote sensing", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85310M (23 October 2012);

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