Foliage temperature extraction from thermal imagery for crop water stress determination

Foliage temperature extraction from thermal imagery for crop water stress determination Crop water stress determination methods from canopy temperatures, derived from the surface energy balance equations, treat the canopy temperature under the assumption that the canopy behaves as a virtual “big-leaf”, covering the ground surface. Introduction of very high-resolution thermal imagery, 0.01–0.3-m pixel size, acquired from low altitude platforms, enabled finely detailed observation of the whole canopy, raising the question how to select the relevant canopy temperatures. One approach is to select the sunlit leaves confirming to the “big leaf” energy balance paradigm. However, thermal imagery alone is incomplete and needs additional marking or synchronized visible imagery for interpretation, which makes the process complicated and expensive. The other approach, used in reference surface based water stress evaluation, is to use full frame pixel statistics without pattern recognition by selecting the mean temperature of the cold fraction from the pixel histogram. That greatly simplifies processing for large-scale aerial thermography. Here are presented the results of experiments conducted in cotton and vine grapes, where both approaches were evaluated simultaneously. Ground referenced thermal and visible images were overlapped, and sunlit, shaded and whole canopy leaves were selected for crop temperature evaluation. The pixel histograms of the same images were analyzed in a two-step method, after discarding soil pixels where their temperature was 7 °C higher than air temperature at step one, and calculation of the mean temperatures of the lowest 33 and 100 % of the remaining pixels for step two. Several crop water stress indices were compared with leaf and stem water potentials and stomatal conductance. Good agreement was found between both image segmentation and histogram analysis methods, demonstrating the suitability of both methods in canopy temperature evaluation for crop water stress evaluation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Foliage temperature extraction from thermal imagery for crop water stress determination

Loading next page...
 
/lp/springer_journal/foliage-temperature-extraction-from-thermal-imagery-for-crop-water-NiaK0jjplT
Publisher
Springer US
Copyright
Copyright © 2013 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-013-9310-0
Publisher site
See Article on Publisher Site

Abstract

Crop water stress determination methods from canopy temperatures, derived from the surface energy balance equations, treat the canopy temperature under the assumption that the canopy behaves as a virtual “big-leaf”, covering the ground surface. Introduction of very high-resolution thermal imagery, 0.01–0.3-m pixel size, acquired from low altitude platforms, enabled finely detailed observation of the whole canopy, raising the question how to select the relevant canopy temperatures. One approach is to select the sunlit leaves confirming to the “big leaf” energy balance paradigm. However, thermal imagery alone is incomplete and needs additional marking or synchronized visible imagery for interpretation, which makes the process complicated and expensive. The other approach, used in reference surface based water stress evaluation, is to use full frame pixel statistics without pattern recognition by selecting the mean temperature of the cold fraction from the pixel histogram. That greatly simplifies processing for large-scale aerial thermography. Here are presented the results of experiments conducted in cotton and vine grapes, where both approaches were evaluated simultaneously. Ground referenced thermal and visible images were overlapped, and sunlit, shaded and whole canopy leaves were selected for crop temperature evaluation. The pixel histograms of the same images were analyzed in a two-step method, after discarding soil pixels where their temperature was 7 °C higher than air temperature at step one, and calculation of the mean temperatures of the lowest 33 and 100 % of the remaining pixels for step two. Several crop water stress indices were compared with leaf and stem water potentials and stomatal conductance. Good agreement was found between both image segmentation and histogram analysis methods, demonstrating the suitability of both methods in canopy temperature evaluation for crop water stress evaluation.

Journal

Precision AgricultureSpringer Journals

Published: Apr 12, 2013

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off