Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψ_stem), assisting in irrigation decision-making for commercial vineyards. However, VI-Ψ_stem correlations are mostly reported at the leaf or canopy scales, using proximal canopy-based sensors or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of high-spatial resolution (3-m) near-daily images acquired from Planet’s nano-satellite constellation to derive VI-Ψ_stem correlations at the vineyard scale. Weekly Ψ_stem was measured along the growing season of 2017 in six vines each in 81 commercial vineyards and in 60 pairs of grapevines in a 2.4 ha experimental vineyard in Israel. The Clip application programming interface (API), provided by Planet, and the Google Earth Engine platform were used to derive spatially continuous time series of four VIs—GNDVI, NDVI, EVI and SAVI—in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψ_stem across the experimental vineyard (Pearson’s-r = 0.45–0.84; N = 60). A simple linear regression model enabled monitoring seasonal changes in Ψ_stem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψ_stem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψ_stem at the vineyard-level (r = 0.78; RMSE = 18.5%; N = 970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψ_stem in Mediterranean vineyards, substantially improving the efficiency of conventional in-field monitoring efforts.

The major factors of wine trade have been showing distinct patterns of temporal trends worldwide in past decades. Wine consumption, production, imports, and exports differ according to their location and classification to Old World and New World wine markets. Using datasets from various sources, this work focused on quantifying long-term trends (1995–2021) of these wine industry factors for each country, including long-term means and temporal trends, using the Mann-Kendall trend test, and resulting in Z-scores. The temporal relationships between these global factors were quantified by applying Pearson correlation to the original values, as well as by correlating the Z-scores. Our findings show that Old World wine consumers and producers (e.g., Spain, France, and Italy) have been experiencing gradual decreasing trends of wine consumption and production. In New World countries, some of the largest wine-consuming countries were found to have strong, significant increases in wine consumption and new wine production markets show rapid growth trends. About 80% of the countries demonstrated increasing trends of wine imports, signifying the impact of globalization on the wine market and the growing demand for foreign wine. Globally, consumption per capita was found to have significantly decreased. Wine production showed a strong, significant, and lagged dependence on wine consumption, which was also related to the temporal trends of wine imports and exports. The major forces driving the wine market are possibly economic growth and wider competition, with climate change acting as a disruptive force.

Premise of the Study: The stem of Vitis vinifera, a climbing vine of global economic importance, is characterized by both wide and narrow vessels and high specific hydraulic conductivity. While the effect of drought stress has been studied in 1‐ and 2‐yr‐old stems, there are few data documenting effects of drought stress on the anatomical structure of the mature, woody stem near the base of the vine. Here we describe mature wood anatomical responses to two irrigation regimes on wood anatomy and specific hydraulic conductivity in Vitis vinifera Merlot vines.Methods: For 4 years, irrigation was applied constantly at low, medium, or high levels, or at alternating levels at two different periods during the growing season, either early spring or late summer, resulting in late season or early spring deficits, respectively. The following variables were measured: trunk diameter, annual ring width and area, vessel diameter, specific hydraulic conductivity and stem water potential.Key Results: High water availability early in the season (late deficit) resulted in vigorous vegetative growth (greater trunk diameter, ring width and area), wider vessels and increased specific hydraulic conductivity. High water availability early in the season caused a shift of the vessel population towards the wider frequency classes. These late deficit vines showed more negative water potential values late in the season than vines that received low but relatively constant irrigation.Conclusions: We concluded that high water availability during vegetative growth period of Vitis increases vessels diameter and hydraulic conductivity and causes the vines to be more vulnerable to drought stress late in the season.

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.