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

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.

[1]  Joan Girona,et al.  Sensitivity of Continuous and Discrete Plant and Soil Water Status Monitoring in Peach Trees Subjected to Deficit Irrigation , 1999 .

[2]  Peter Droogers,et al.  Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing , 2017 .

[3]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[4]  E. Fereres,et al.  Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard , 2013, Precision Agriculture.

[5]  P. Zarco-Tejada,et al.  Scheduling vineyard irrigation based on mapping leaf water potential from airborne thermal imagery , 2013 .

[6]  Pablo J. Zarco-Tejada,et al.  Assessing structural effects on PRI for stress detection in conifer forests , 2011 .

[7]  E. Fereres,et al.  Plant indicators for scheduling irrigation of young olive trees , 2002, Irrigation Science.

[8]  J. Alarcón,et al.  Effects of regulated deficit irrigation on physiology and fruit quality in apricot trees , 2010 .

[9]  Pablo J. Zarco-Tejada,et al.  Assessing Canopy PRI for Water Stress detection with Diurnal Airborne Imagery , 2008 .

[10]  Pablo J. Zarco-Tejada,et al.  Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops , 2004 .

[11]  J. Pereira,et al.  How plants cope with water stress in the field. Photosynthesis and growth. , 2002, Annals of botany.

[12]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[13]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[14]  D. Mulla Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[15]  Saleh Taghvaeian,et al.  Water balance of irrigated areas: a remote sensing approach , 2011 .

[16]  Ismael Moya,et al.  Photochemistry, remotely sensed physiological reflectance index and de-epoxidation state of the xanthophyll cycle in Quercus coccifera under intense drought , 2008, Oecologia.

[17]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[18]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[19]  A. Bolten,et al.  INTRODUCING A LOW-COST MINI-UAV FOR THERMAL- AND MULTISPECTRAL-IMAGING , 2012 .

[20]  D. Intrigliolo,et al.  Performance of various water stress indicators for prediction of fruit size response to deficit irrigation in plum , 2006 .

[21]  T W Tibbitts,et al.  Measurement of ozone injury by determination of leaf chlorophyll concentration. , 1977, Plant physiology.

[22]  Stewart J. Cohen,et al.  Climate Change 2014: Impacts,Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2014 .

[23]  Robin Gebbers,et al.  Crop sensor readings in winter wheat as affected by nitrogen and water supply , 2013 .

[24]  R. S. Alberte,et al.  Water stress effects on the content and organization of chlorophyll in mesophyll and bundle sheath chloroplasts of maize. , 1977, Plant physiology.

[25]  W. Inskeep,et al.  Extinction coefficients of chlorophyll a and B in n,n-dimethylformamide and 80% acetone. , 1985, Plant physiology.

[26]  C. Field Climate change 2014 : impacts, adaptation and vulnerability : Working Group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change , 2014 .

[27]  J. Flexas,et al.  UAVs challenge to assess water stress for sustainable agriculture , 2015 .

[28]  Pablo J. Zarco-Tejada,et al.  Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent , 2012 .

[29]  Elias Fereres,et al.  Irrigation scheduling protocols using continuously recorded trunk diameter measurements , 2001, Irrigation Science.

[30]  Pablo J. Zarco-Tejada,et al.  Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery , 2010 .

[31]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

[32]  P. Zarco-Tejada,et al.  A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index , 2013 .