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| Titel |
Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate |
| VerfasserIn |
J. Chirouze, G. Boulet, L. Jarlan, R. Fieuzal, J. C. Rodriguez, J. Ezzahar, S. Er-Raki, G. Bigeard, O. Merlin, J. Garatuza-Payan, C. Watts, G. Chehbouni |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 3 ; Nr. 18, no. 3 (2014-03-27), S.1165-1188 |
| Datensatznummer |
250120314
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| Publikation (Nr.) |
 copernicus.org/hess-18-1165-2014.pdf |
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| Zusammenfassung |
| Instantaneous evapotranspiration rates and surface water stress levels can
be deduced from remotely sensed surface temperature data through the surface
energy budget. Two families of methods can be defined: the contextual
methods, where stress levels are scaled on a given image between hot/dry and
cool/wet pixels for a particular vegetation cover, and single-pixel methods,
which evaluate latent heat as the residual of the surface energy balance for
one pixel independently from the others. Four models, two contextual (S-SEBI
and a modified triangle method, named VIT) and two single-pixel (TSEB, SEBS)
are applied over one growing season (December–May) for a 4 km × 4 km
irrigated agricultural area in the semi-arid northern Mexico. Their
performance, both at local and spatial standpoints, are compared relatively
to energy balance data acquired at seven locations within the area, as well
as an uncalibrated soil–vegetation–atmosphere transfer (SVAT) model forced
with local in situ data including observed irrigation and rainfall amounts.
Stress levels are not always well retrieved by most models, but S-SEBI as
well as TSEB, although slightly biased, show good performance. The drop in
model performance is observed for all models when vegetation is senescent,
mostly due to a poor partitioning both between turbulent fluxes and between
the soil/plant components of the latent heat flux and the available energy.
As expected, contextual methods perform well when contrasted soil moisture
and vegetation conditions are encountered in the same image (therefore,
especially in spring and early summer) while they tend to exaggerate the
spread in water status in more homogeneous conditions (especially in
winter). Surface energy balance models run with available remotely sensed
products prove to be nearly as accurate as the uncalibrated SVAT model
forced with in situ data. |
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