Mapping water status based on aerial thermal imagery: comparison of methodologies for upscaling from a single leaf to commercial fields

Y. Cohen*, V. Alchanatis, Y. Saranga, O. Rosenberg, E. Sela, A. Bosak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


Aerial thermal remote sensing can provide a means for collecting spatial plant water status data. Many studies have shown their potential in irrigation management but the adaptation of this technology is not straight forward. In this paper, knowledge accumulated in recent years on thermal imagery analysis methodology for water status mapping is summarized aiming at indicating alternatives to calculate the Crop Water Stress Index (CWSI) for commercial scale water status mapping. Based on literature overview, four forms of wet-baselines to calculate CWSI were selected, namely: artificial wet reference surface, two theoretical calculations and a statistical bio-indicator. These baselines were used to calculate CWSI based on multi-temporal aerial thermal images of cotton fields. CWSI based on a statistical bio-indicator and one of the theoretical wet-baselines provided the best correlations. It is argued though that the statistical one is preferable since it includes the plant characteristics and it is farmer-friendly. Based on bio-indicators, leaf water potential maps of three commercial fields were produced on several dates through the season. Water status spatial patterns were not static and the effect of static factors like sandy soil patches also changed through the season. The maps show the importance of in-season variability mapping for rational irrigation management. To improve current variable-rate irrigation, the concept of in-season irrigation management zones (IMZ) based on thermal-images should be considered and integrated with the delineation of static irrigation IMZ.

Original languageAmerican English
Pages (from-to)801-822
Number of pages22
JournalPrecision Agriculture
Issue number5
StatePublished - 1 Oct 2017

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media New York.


  • CWSI
  • Cotton
  • Dry-baseline
  • Dynamic irrigation management zones
  • LWP
  • Water status variability
  • Wet-baseline


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