In the current scenario of worldwide limited water supplies, conserving water is a major concern in agricultural areas. Characterizing within-orchard spatial heterogeneity in water requirements would assist in improving irrigation water use efficiency and conserve water. The crop water stress index (CWSI) has been successfully used as a crop water status indicator in several fruit tree species. In this study, the CWSI was developed in three Prunus persica L. cultivars at different phenological stages of the 2012 to 2014 growing seasons, using canopy temperature measurements of well-watered trees. The CWSI was then remotely estimated using high-resolution thermal imagery acquired from an airborne platform and related to leaf water potential (Ѱ_L) throughout the season. The feasibility of mapping within-orchard spatial variability of Ѱ_L from thermal imagery was also explored. Results indicated that CWSI can be calculated using a common non-water-stressed baseline (NWSB), upper and lower limits for the entire growing season and for the three studied cultivars. Nevertheless, a phenological effect was detected in the CWSI vs. Ѱ_L relationships. For a specific given CWSI value, Ѱ_L was more negative as the crop developed. This different seasonal response followed the same trend for the three studied cultivars. The approach presented in this study demonstrated that CWSI is a feasible method to assess the spatial variability of tree water status in heterogeneous orchards, and to derive Ѱ_L maps throughout a complete growing season. A sensitivity analysis of varying pixel size showed that a pixel size of 0.8 m or less was needed for precise Ѱ_L mapping of peach and nectarine orchards with a tree crown area between 3.0 to 5.0 m².

[Background and Aims] Mapping the spatial variability of vine water status within a vineyard is necessary for the efficient management of irrigation water. The objective of this study was to determine whether estimates of remotely sensed leaf water potential (Ψrem) could be employed as a precise tool for scheduling irrigation at the irrigation sector level throughout the season.[Methods and Results] Three irrigation treatments were applied in a 16-ha commercial vineyard to analyse the performance of the proposed methodology for monitoring regulated deficit irrigation strategies, and to evaluate the required frequency of the acquisition of thermal images for irrigation scheduling. An aircraft equipped with a thermal sensor flew over the vineyard throughout the season, and the averaged Ψrem of each irrigation sector was used as the irrigation trigger. The acquisition of about five or six Ψrem maps over the season is recommended. The starting date for acquiring thermal images depends on canopy vegetation size and on the difficulty of extracting pure vegetation pixels. The effect of acquiring thermal imagery on days after rainfall or with low vapour pressure deficits affected the estimation of Ψrem, and these constraints need to be considered for feasible irrigation purposes.[Conclusions] Remotely sensed leaf water potential was successfully used as an irrigation trigger to adopt regulated deficit irrigation strategies without any negative effect on yield and wine composition.[Significance of the Study] This study presented a promising and powerful method for scheduling irrigation throughout the season at vineyard level based on estimates of remotely sensed leaf water potential.This research was supported by the Spanish Ministry of Economy and Competitiveness, under Project INNPACTO IPT-2011-1786-060000.Peer reviewe

There is little information on the sensitivity of berry composition to early-season water stress and how it compares to the effects of late-season stress. This study aimed to quantify the effects of water stress on berry growth and composition of Cabernet Sauvignon grapevine at three phenological stages: anthesis to fruit set, fruit set to veraison, and veraison to harvest. Potted vines were used to facilitate imposing water stress early in the season. Four irrigation levels (0%, 25%, 50%, and 100% of calculated crop evapotranspiration, ET(c)) were applied and midday leaf water potential and leaf gas exchange were measured. Berry composition was evaluated by measuring titratable acidity and concentrations of soluble solids, anthocyanins, and polyphenols. Water stress decreased net CO(2) exchange rate and vine green leaf area. Berry composition significantly correlated with the vine water status, but the nature of the relationship depended on the phenological stage and on the parameter measured. Berry composition (in terms of concentration of anthocyanins and polyphenols) was improved when no water stress occurred from anthesis to fruit set (irrigation replacing 100% of ET(c)), with mild water stress between fruit set and veraison (irrigation replacing 25% and 50% of ET(c)), and with moderate to severe water stress in postveraison (irrigation replacing 0% of ET(c))

In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (K_cb), or potential crop water use. The soil water evaporation coefficient (K_e) was measured from microlysimeters. The water stress coefficient (K_s) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψ_stem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum K_cb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R² ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day⁻¹ for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions.