One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

Remote sensing satellite platforms provide accurate temporal and spatial information useful in viticulture with an increasing interest in their use. This study aims to identify the possibilities of freely available and with frequent revisit time Sentinel-2 satellites, to monitor vine growth at regional scale on a vine-growing Protected Designation of Origin (PDO) zone during the growing season of the year 2019. This study aims to: (i) investigate through several Vegetation Indices (VIs) the vine growth differences across the zone and relations with topographic parameters; (ii) identify VIs that best recognize differences on subzones of different climatic conditions; (iii) explore the effectiveness of the Sentinel-2 data monitoring management applications. A total of 27 vineyards were selected for field and satellite data collection. Several VIs have been calculated per vineyard from a 20-date time series dataset. VIs showed high negative correlation with topographic parameter of elevation on the flowering stage. The analysis of variance between the VIs of the subzones showed that these regions have statistically significant differences, that most VIs can expose on the flowering and harvest stage, and only Normalized Difference Vegetation Index (NDVI) and VIs using Red-Edge bands during the veraison period. Sentinel-2 data show great effectiveness on monitoring management applications (tillage and trimming).

In this study, a spatial analysis of agronomic and remote sensing data is carried out to derive accurate rice crop yield estimation. The variability of a series of vegetation indices (VIs) was calculated from remote sensing data obtained via a commercial UAS platform (e-Bee) at four dates (per stage of development), and the development of estimation models was conducted. The study area is located in the region of Chalastra (municipality of Thessaloniki, North Greece) and the primary data were obtained during the 2016 growing season. These data include ultra-high resolution remote sensing multispectral images of 18 plots totaling 58 hectares of Ronaldo and Gladio rice crop varieties, 97 sample point data related to yield, and many other pieces of information recorded in the producer’s field log. Ten simple and compound VIs were calculated, and the evolution of their values during the growing season as well as their comparative correlation were studied. A study of the usability of each VI was conducted for the different phenological stages of the cultivation and the variance of VIs and yield; the more correlated VIs were identified. Furthermore, three types of multitemporal VI were calculated from combinations of VIs from different dates, and their contribution to improving yield prediction was studied. As Ronaldo is a Japonica type of rice variety and Gladio is Indica type, they behave differently in terms of maturation time (Gladio is approximately 20 days earlier) and the value of every VI is affected by changes in plant physiology and phenology. These differences between the two varieties are reflected in the multitemporal study of the single-date VIs but also in the study of the values of the multitemporal VIs. In conclusion, Ronaldo’s yield is strongly dependent on multitemporal NDVI (VI6th July + VI30 Aug, R2 = 0.76), while Gladio’s yield is strongly dependent on single-date NDVI (6 July, R2 = 0.88). The compound VIs RERDVI and MCARI1 have the highest yield prediction (R2 = 0.77) for Ronaldo (VI6th July + VI30 Aug) and Gladio (R2 = 0.95) when calculated in the booting stage, respectively. For the Ronaldo variety, the examination of the multitemporal VIs increases yield prediction accuracy, while in the case of the Gladio variety the opposite is observed. The capabilities of multitemporal VIs in yield estimation by combining UAVs with more flights during the different growth stages can improve management and the cultivation practices.