1,164
Views
35
CrossRef citations to date
0
Altmetric
Research Article

Remote sensing techniques for predicting evapotranspiration from mixed vegetated surfaces

, , , &
Pages 380-393 | Received 24 Oct 2013, Accepted 26 Feb 2014, Published online: 13 May 2014
 

Abstract

Evapotranspiration (ET) is a key component of the hydrological cycle however it is also the most difficult factor to quantify. In recent decades, estimating ET has been improved by advances in remote sensing, particularly in agricultural studies. However, quantifying ET from mixed vegetation environs, particularly urban parklands, is still challenging due to the heterogeneity of plant species, canopy covers, microclimates, and because of costly methodological requirements. Several studies have recently been conducted in agriculture and forestry which may be useful for mixed landscape vegetation studies with some modifications. This review describes general remote sensing-based approaches to estimate ET and describes their advantages and disadvantages. Most of these approaches need extensive time investment, medium to high skill levels and are quite expensive. However, in addition to the reviewed methods, the authors recommend combining remotely sensed vegetation indices and ground-based techniques for ET estimation of mixed landscape vegetation such as urban parklands.

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 239.00 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.