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Proceedings Paper

A hybrid approach for estimating spatial evapotranspiration from satellite imagery
Author(s): C. M. U. Neale; M. P. González Dugo; L. Mateos; W. P. Kustas; Y Kaheil
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Paper Abstract

Two common approaches for estimating crop evapotranspiration (ET) using satellite imagery are the reflectance-based crop coefficient method and the energy balance method. The reflectance-based crop coefficient method relates a reflectance-based vegetation index such as the soil adjusted vegetation index (SAVI) to ET basal crop coefficients such as those described by Wright (1982) [1] and the FAO 56 manual [2]. A time-series of remotely sensed inputs is then used to build the crop coefficient curve in each field being monitored. In order to obtain actual ET, a water balance must be maintained in the root zone of the crop in order to make the appropriate adjustments due to soil moisture deficits and wet soil surface from irrigation and/or rain. Ground meteorological data must be provided by a weather station located in the modeled area for the estimation of reference ET. In the energy balance approach, surface temperatures are used in the estimation of sensible heat fluxes and depending on the complexity of the model, different methods are used to either handle the aerodynamic temperature term or deal with sparse canopies (empirical approaches, two-source model, SEBAL model). Remotely sensed inputs are also used for the estimation of net radiation and soil heat flux, with latent heat flux (ET) obtained as a residual from the energy balance equation. The energy balance approach results in the actual ET being estimated directly. Instantaneous values of ET must be extrapolated to the entire day and over time in between satellite overpass inputs. This paper describes a hybrid approach that uses both methods in combination to monitor actual ET over a growing season for irrigated and non-irrigated crops. The model has been coded in an ArcGIS environment, using visual basic for the calculations. This paper describes the modeling environment and coded ET models within and presents some application results.

Paper Details

Date Published: 9 October 2007
PDF: 8 pages
Proc. SPIE 6742, Remote Sensing for Agriculture, Ecosystems, and Hydrology IX, 674209 (9 October 2007); doi: 10.1117/12.740261
Show Author Affiliations
C. M. U. Neale, Utah State Univ. (United States)
M. P. González Dugo, Instituto de Investigación y Formación Agraria y Pesquera (Spain)
L. Mateos, Instituto de Agricultura Sostenible (Spain)
W. P. Kustas, U.S. Dept. of Agriculture (United States)
Y Kaheil, Utah State Univ. (United States)


Published in SPIE Proceedings Vol. 6742:
Remote Sensing for Agriculture, Ecosystems, and Hydrology IX
Christopher M. U. Neale; Manfred Owe; Guido D'Urso, Editor(s)

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