Precision Agriculture

, Volume 19, Issue 1, pp 178–193 | Cite as

Evaluating the performance of xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species

  • C. Ballester
  • P. J. Zarco-Tejada
  • E. Nicolás
  • J. J. Alarcón
  • E. Fereres
  • D. S. Intrigliolo
  • V. Gonzalez-Dugo
Article
First Online:

Abstract

This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (gs), stem water potential (Ψs) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R700/R670), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI(570–515)] and the normalized PRI (PRInorm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψs yielded the best correlations with PRI and PRI(570–515) (r2 = 0.53) in almond trees, with TCARI/OSAVI (r2 = 0.88) in apricot trees and with PRInorm, R700/R670 and NDVI (r2 from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (Tc) and PRI(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψs was the most sensitive indicator at midday. PRInorm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, gs and, particularly, Ψs (r2 = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.

Keywords

Fruit crop Multispectral imagery Remote sensing Water stress detection 

Abbreviations

gs

Stomatal Conductance

Ψs

Stem Water Potential

CWSI

Crop Water Stress Index

Tc

Canopy Temperature

UAS

Unmanned Aerial System

NDVI

Normalized Difference Vegetation Index

R700/R670

Red Edge Ratio (reflectance at 700 nm divided by the reflectance at 670 nm)

TCARI

Transformed Chlorophyll Absorption in Reflectance Index

OSAVI

Optimized Soil Adjusted Vegetation Index

PRI

Photochemical Reflectance Index (using reflectance at 530 and 570 nm)

PRInorm

Normalized Photochemical Reflectance Index

PRI(570–515)

Photochemical Reflectance Index (using reflectance at 515 and 570 nm)

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Notes

Acknowledgements

Authors acknowledge K. Gutierrez, D. Notario, P.A Nortes, M.A. Jiménez-Bello and A. Vera for their technical support. This work was funded by the Spanish Ministry of Science and Innovation for the Projects CONSOLIDER CSD2006-0067, AGL2009-13105, AGL2010-17553 and AGL2013-49047-C2-2-R and the European Union through Projects Interreg SUDOE IVB “Telerieg”, IRRIQUAL (EU-FP6-FOOD-CT-2006-023120) and SIRRIMED (KBBE-2009-1-2-03, Proposal No. 245159). We are also grateful to two SENECA Projects (05665/PI/07 and 11872/PI/09) and SENECA—Excelencia Científica (19903/GERM/15), for providing funds to finance this research.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • C. Ballester
    • 1
    • 2
  • P. J. Zarco-Tejada
    • 3
  • E. Nicolás
    • 4
  • J. J. Alarcón
    • 4
  • E. Fereres
    • 3
    • 5
  • D. S. Intrigliolo
    • 1
    • 4
  • V. Gonzalez-Dugo
    • 3
  1. 1.Instituto Valenciano de Investigaciones Agrarias (IVIA)MoncadaSpain
  2. 2.Centre for Regional and Rural Futures (CeRRF)Deakin UniversityGriffithAustralia
  3. 3.Instituto de Agricultura Sostenible (IAS)Consejo Superior de Investigaciones CientíficasCórdobaSpain
  4. 4.Centro de Edafología y Biología Aplicada del Segura (CEBAS)MurciaSpain
  5. 5.Departamento de AgronomíaUniversidad de Córdoba (UCO)CórdobaSpain

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