Elsevier

Remote Sensing of Environment

Volume 114, Issue 2, 15 February 2010, Pages 286-298
Remote Sensing of Environment

Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery

https://doi.org/10.1016/j.rse.2009.09.006Get rights and content

Abstract

A methodology for the assessment of fruit quality in crops subjected to different irrigation regimes is presented. High spatial resolution multispectral and thermal airborne imagery were used to monitor crown temperature and the Photochemical Reflectance Index (PRI) over three commercial orchards comprising peach, nectarine and orange fruit trees during 2008. Irrigation regimes included sustained and regulated deficit irrigation strategies, leading to high variability of fruit quality at harvest. Stem water potential was used to monitor individual tree water status on each study site. Leaf samples were collected for destructive sampling of xanthophyll pigments to assess the relationship between the xanthophyll epoxidation state (EPS) and PRI at leaf and airborne-canopy level. At harvest, fruit size, Total Soluble Solids (TSS) and Tritatable Acidity (TA) were measured to characterize fruit quality. A statistically significant relationship between EPS and PRI was found at the leaf (r2 = 0.81) and canopy level (r2 = 0.41). Airborne-derived crown PRI calculated from the imagery acquired during the fruit growth was related to the ratio of the total soluble solids normalized by the tritatable acidity (TSS/TA), an indicator of fruit quality measured on the same trees, yielding a coefficient of determination of r2 = 0.50. The relationship between the integral of PRI time-series and TSS/TA yielded a coefficient of determination of r2 = 0.72 (peach) and r2 = 0.61 (nectarines). On the contrary, the relation between TSS/TA and the time-series of crown thermal imagery was very weak (r2 = 0.21 and 0.25 respectively). These results suggest that a physiological remote sensing indicator related to photosynthesis, such as PRI, is more appropriate for fruit quality assessment than crown temperature, the established method of water stress detection, which is more related to crown transpiration. A radiative transfer modelling study was conducted to assess the potential validity of this methodology for fruit quality assessment when using medium spatial resolution imagery. The analysis shows important effects of soil and shadows on the PRI vs EPS relationship used for fruit quality assessment if non-pure crown reflectance was extracted from the imagery.

Introduction

Twenty-five years ago, thermal information was chosen for the remote sensing of water stress in crops (Jackson et al., 1981, Idso, 1982a, Idso, 1982b) because the spectral vegetation indices that existed at that time were not nearly as sensitive to water deficits as those derived from canopy temperature (Jackson et al., 1983). Thermal remote sensing of water stress was first performed using spectrometers at ground level (Idso et al., 1981, Jackson et al., 1977, Jackson et al., 1981), but other approaches have been developed more recently. These included the use of airborne thermal imagery (Cohen et al., 2005, Leinonen and Jones, 2004, Sepulcre-Cantó et al., 2007) and satellite thermal information in combination with 3D radiative transfer models to understand the effects of scene thermal components on large ASTER pixels (Sepulcre-Cantó et al., 2009). Notwithstanding the advances in thermal detection, the visible part of the spectrum has also been useful for pre-visual water stress detection based on indices that use bands located at specific wavelengths where photosynthetic pigments are affected by stress condition. This is the case of the Photochemical Reflectance Index (PRI) (Gamon et al., 1992) that has been proposed to assess vegetation water stress based on xanthophyll composition changes (Peguero-Pina et al., 2008, Suárez et al., 2008, Suárez et al., 2009, Thenot et al., 2002). The PRI was presented as an indicator of the epoxidation state of the xanthophylls pool or, what is the same, the proportion of violaxanthin that has been converted into zeaxanthin under stress conditions (Gamon et al., 1992). For water stress detection, PRI could be an alternative to thermal remote sensing, enabling the use of low-cost imaging sensors with high spatial resolution capabilities that are not possible in the thermal domain (Suárez et al., 2008, Suárez et al., 2009).

In addition, the PRI is an index that was first formulated as an indicator of photosynthetic efficiency, but is also an indicator of photosynthesis rate through light use efficiency (Asner et al., 2005, Drolet et al., 2005, Fuentes et al., 2006, Guo and Trotter, 2004, Nakaji et al., 2006, Nichol et al., 2000, Nichol et al., 2002, Serrano and Peñuelas, 2005, Sims et al., 2006, Strachan et al., 2002, Trotter et al., 2002) and through chlorophyll fluorescence (Dobrowsky et al., 2005, Evain et al., 2004, Nichol et al., 2006). Therefore, PRI in addition to being a water stress indicator, is also directly related to several physiological processes involved in the photosynthetic system.

The remote detection and monitoring of water stress is critical in many world areas where water scarcity is a major constraint to irrigated agriculture, and is forcing farmers to reduce irrigation water use via deficit irrigation (DI) (Fereres & Soriano, 2007). One of the DI approaches is the regulated deficit irrigation (RDI), where water deficits are imposed only during the crop developmental stages that are the least sensitive to water stress (Chalmers et al., 1981). This practice was originally proposed to control the vegetative vigour in high-density orchards to reduce production costs and to improve fruit quality. However, it also saves irrigation water, with the concomitant benefits of reduced drainage losses (Fereres & Soriano, 2007). It has long been known that tree water deficits affect fruit quality parameters (Veihmeyer, 1927). However, when water deficits are imposed as in RDI, yield and fruit size are not affected (Girona, 2002), while some quality parameters such as total soluble sugars and total acidity increase (Crisosto et al., 1994, Girona et al., 2003, Mills et al., 1994). The responses to RDI are variable depending on the timing and severity of water deficits (Marsal and Girona, 1997, Girona et al., 2003) which vary within a given orchard; thus the need for remote sensing tools that could assist in monitoring stress over entire orchards. Additionally, the changes in irrigation depths with time and the lack of uniformity in water application during the irrigation period emphasize the need for a methodology that would cover the entire season, integrating the short-term variations in tree water status. One option would be to use an integrated measure over time of tree water status (Myers, 1988, Ginestar and Castel, 1996). González-Altozano and Castel (1999) related the time integral of stem water potential with yield and fruit quality parameters in citrus. Baeza et al. (2007) attempted the same approach on vineyards, finding a correlation between a water stress-integral and final berry size, although not with sugar composition. Although the relationships between water stress and fruit quality has been widely studied, the conclusion is that there is a lack of reliable indicators that predict with precision final fruit quality, and therefore there is a need for further research concerning potential fruit quality indicators.

Remote sensing of fruit quality has been attempted by several means such as by determining the vigour or total leaf area in vineyards (Johnson et al., 2001, Johnson et al., 2003, Lamb et al., 2004); by relating quality parameters in water-stressed mandarin trees to spectral changes in the red and green channels (Kriston-Vizi et al., 2008), and by using high spatial resolution airborne thermal imagery to outline relationships of olive fruit size, weight, and oil content against thermal water stress indicators (Sepulcre-Cantó et al., 2007).

In this work, the PRI has been used to assess fruit quality parameters in peach and orange orchards under various water regimes. A time-series of airborne PRI imagery over a peach and an orange orchard under different irrigation treatments were acquired and related to fruit quality at harvest. Furthermore, a 3D radiative transfer model was used to assess the applicability of this method to medium resolution PRI imagery for extended monitoring of crops at larger scales. For this purpose, simulations using different soil backgrounds were conducted and the output spectral information was evaluated at different spatial resolutions.

Section snippets

Study sites

The experimental areas are located in Western Andalucía, Spain, a region of Mediterranean climate characterized by warm and dry summers and cool and wet winters, with an average annual rainfall of over 550 mm.

The first study site was located on a commercial peach orchard planted in 1990 in a 5 × 3.3 m grid on a deep soil with moderately high water holding capacity and classified as Typic Xerofluvents in Cordoba, Spain (37.5°N, 4.9°W) (Fig. 1a). Two experiments were carried out in this location. One

Results and discussion

Fig. 3a shows that, at the leaf level, the EPS calculated from pigment determination methods was well correlated with leaf PRI calculated from the same leaves collected in the field. Leaves with higher EPS values, corresponding to a high concentration of the photosynthetic active pigment violaxanthin over the whole xanthophyll pool, and consequently less stressed, presented lower PRI values. Lower values of PRI are the consequence of lower absorption at 530 nm using the presented formulation of

Conclusions

This study demonstrates the link between the epoxidation state of the xanthophyll cycle and the fruit quality measured in orchards under different irrigation regimes, enabling the remote detection of fruit quality as a function of water stress using high-resolution airborne PRI. The PRI index measured at leaf scale was in agreement with the epoxidation state of the xanthophyll cycle calculated from destructive sampling. In addition, the airborne image-derived PRI values calculated from pure

Acknowledgements

Financial support from the Spanish Ministry of Science and Innovation (MCI) for the projects AGL2005-04049, EXPLORA-INGENIO AGL2006-26038-E/AGR, CONSOLIDER CSD2006-67, and AGL2003-01468, and from Gobierno de Aragón (group A03) is gratefully acknowledged, and support in-kind provided by Bioiberica through the project PETRI PET2005-0616. Technical support from UAV Navigation and Tetracam Inc. is also acknowledged. M. Medina, C. Ruz, R. Gutierrez, A. Vera, D. Notario, I. Calatrava and M. Ruiz

References (67)

  • A.R. Huete

    A soil-adjusted vegetation index (SAVI)

    Remote Sensing of Environment

    (1988)
  • S.B. Idso

    Humidity measurement by Infrared Thermometry. (1982)

    Remote Sensing of Environment

    (1982)
  • S.B. Idso

    Non-water-stressed baselines: A key to measuring and interpreting plant water stress

    Agricultural Meteorology

    (1982)
  • S.B. Idso et al.

    Normalizing the stress-degree-day parameter for environmental variability

    Agricultural and Forest Meteorology

    (1981)
  • R.D. Jackson et al.

    Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres

    Remote Sensing of Environment

    (1983)
  • S. Jacquemoud et al.

    PROSPECT: A model of leaf optical properties spectra

    Remote Sensing of Environment

    (1990)
  • L.F. Johnson et al.

    Mapping vineyard leaf area with multispectral satellite imagery

    Computers and Electronics in Agriculture

    (2003)
  • J. Kriston-Vizi et al.

    Assessment of the water stress status of mandarin and peach canopies using visible multispectral imagery

    Biosystems Engineering

    (2008)
  • C.J. Nichol et al.

    Remote sensing of photosynthetic-light-use efficiency of boreal forest

    Agricultural and Forest Meteorology

    (2000)
  • J. Qi et al.

    A modified soil adjusted vegetation index (MSAVI)

    Remote Sensing of Environment

    (1994)
  • G. Rondeaux et al.

    Optimization of soil-adjusted vegetation indices

    Remote Sensing of Environment

    (1996)
  • G. Sepulcre-Cantó et al.

    Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER

    Remote Sensing of Environment

    (2007)
  • G. Sepulcre-Cantó et al.

    Detecting water status in open canopies with thermal ASTER imagery and DART radiative transfer simulation

    Agricultural and Forest Meteorology

    (2009)
  • D.A. Sims et al.

    Parallel adjustment in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem

    Remote Sensing of Environment

    (2006)
  • I.B. Strachan et al.

    Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance

    Remote Sensing of Environment

    (2002)
  • L. Suárez et al.

    Modelling PRI for water stress detection using radiative transfer models

    Remote Sensing of Environment

    (2009)
  • L. Suárez et al.

    Assessing canopy PRI for water stress detection with diurnal airborne imagery

    Remote Sensing of Environment

    (2008)
  • A. Abadía et al.

    Iron and plant pigments

  • C.V.M. Barton et al.

    Remote sensing of canopy light use efficiency using the photochemical reflectance index. Model and analysis

    Remote Sensing of Environment

    (2001)
  • J.A.J. Berni et al.

    Thermal and narrow-band multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle

    IEEE Transactions on Geoscience and Remote Sensing

    (2009)
  • D.J. Chalmers et al.

    Control of peach tree growth and productivity by regulated water supply, tree density and summer pruning

    Journal of the American Society of Horticultural Sciences

    (1981)
  • Y. Cohen et al.

    Estimation of leaf potential by thermal imagery and spatial analysis

    Journal of Experimental Botany

    (2005)
  • C.H. Crisosto et al.

    Irrigation regimes affect fruit soluble solids concentration and rate of water loss of ‘O'Henry’ peaches

    Horticultural Sciences

    (1994)
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