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Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates are discussed. Future research should aim at developing thermal imaging into an irrigation scheduling tool applicable to different crops.

The Craig-Gordon model (C-G model) [H. Craig, L.I. Gordon. Deuterium and oxygen 18 variations in the ocean and the marine atmosphere. In Stable Isotopes in Oceanographic Studies and Paleotemperatures, E. Tongiorgi (Ed.), pp. 9-130, Laboratorio di Geologia Nucleare, Pisa (1965).] has been synonymous with the isotope effects associated with the evaporation of water from surface waters, soils, and vegetations, which in turn constitutes a critical component of the global water cycle. On the occasion of the four decades of its successful applications to isotope geochemistry and hydrology, an attempt is made to: (a) examine its physical background within the framework of modern evaporation models, (b) evaluate our current knowledge of the environmental parameters of the C-G model, and (c) comment on a general strategy for the use of these parameters in field applications. Despite its simplistic representation of evaporation processes at the water-air interface, the C-G model appears to be adequate to provide the isotopic composition of the evaporation flux. This is largely due to its nature for representing isotopic compositions (a ratio of two fluxes of different isotopic water molecules) under the same environmental conditions. Among many environmental parameters that are included in the C-G model, accurate description and calculations are still problematic of the kinetic isotope effects that occur in a diffusion-dominated thin layer of air next to the water-air interface. In field applications, it is of importance to accurately evaluate several environmental parameters, particularly the relative humidity and isotopic compositions of the 'free-atmosphere', for a system under investigation over a given time-scale of interest (e.g., hourly to daily to seasonally). With a growing interest in the studies of water cycles of different spatial and temporal scales, including paleoclimate and water resource studies, the importance and utility of the C-G model is also likely to grow in the future.

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