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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Hydrological Process...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Hydrological Processes
Article . 2011 . Peer-reviewed
License: Wiley Online Library User Agreement
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Satellite‐based ET estimation in agriculture using SEBAL and METRIC

Authors: Richard Allen; Ayse Irmak; Ricardo Trezza; Jan M. H. Hendrickx; Wim Bastiaanssen; Jeppe Kjaersgaard;

Satellite‐based ET estimation in agriculture using SEBAL and METRIC

Abstract

AbstractSurface Energy Balance Algorithms for Land (SEBAL) and Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) are satellite‐based image‐processing models that calculate evapotranspiration (ET) as a residual of a surface energy balance. Both models are calibrated using inverse modelling at extreme conditions approach to develop image‐specific estimations of the sensible heat flux (H) component of the surface energy balance and to effectively remove systematic biases in net radiation, soil heat flux, radiometric temperature and aerodynamic estimates. SEBAL and METRIC express the near‐surface temperature gradient as an indexed function of radiometric surface temperature, eliminating the need for absolutely accurate surface temperature and the need for air temperature measurements. Slope and aspect functions and temperature lapsing are used in METRIC applications in mountainous terrains. SEBAL and METRIC algorithms are designed for relatively routine application by trained professionals familiar with energy balance, aerodynamics and basic radiation physics. The primary inputs for the models are short‐wave and long‐wave (thermal) images from satellite (e.g. Landsat and MODIS), a digital elevation model and ground‐based weather data measured within or near the area of interest. ET ‘maps’ (i.e. images) developed using Landsat images provide means to quantify ET on a field basis in terms of both rate and spatial distribution. METRIC takes advantage of calibration using weather‐based reference ET so that both calibration and extrapolation of instantaneous ET to 24‐h and longer periods compensate for regional advection effects where ET can exceed daily net radiation. SEBAL and METRIC have advantages over conventional methods of estimating ET using crop coefficient curves or vegetation indices in that specific crop or vegetation type does not need to be known and the energy balance can detect reduced ET caused by water shortage, salinity or frost as well as evaporation from bare soil. Copyright © 2011 John Wiley & Sons, Ltd.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
344
Top 1%
Top 1%
Top 10%
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