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