258
Views
23
CrossRef citations to date
0
Altmetric
Original Articles

Remote sensing based estimation of evapotranspiration rates

&
Pages 2535-2551 | Received 19 Jun 2002, Accepted 18 Aug 2003, Published online: 12 May 2010
 

Abstract

A remote sensing based method is presented for calculating evapotranspiration rates (λE) using standard meteorological field data and radiometric surface temperature recorded for bare soil, maize and wheat canopies in Denmark. The estimation of λE is achieved using three equations to solve three unknowns; the atmospheric resistance (rae ), the surface resistance (rs ) and the vapour pressure at the surface (es ) where the latter is assessed using an empirical expression. The method is applicable, without modification, to dense vegetation and moist soil, but for a dry bare soil, where the effective source of water vapour is below the surface, the temperature of the evaporating front (Ts *) can not be represented by the measured surface temperature (Ts ). In this case (Ts -Ts *) is assessed as a linear function of the difference between surface temperature and air temperature. The calculated λE is comparable to latent heat fluxes recorded by the eddy covariance technique.

Acknowledgments

The paper was prepared within the projects EO-flux-budget and RS-model which is financed by the Danish research council, Copenhagen. Information on leaf area index and precipitation was kindly provided by Anton Thomsen and Tom Jensen from The Danish Institute of Agricultural Sciences in Foulum, Denmark.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 689.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.