Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

Abstract

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics.

Medium-Resolution Multispectral Data from Sentinel-2 to Assess the Damage and the Recovery Time of Late Frost on Vineyards

“…Medium-resolution data may be the most cost-effective to inspect large areas, particularly in the case of extreme weather events. Previous studies provided evidence of the agreement between VIs calculated from high-and medium-resolution images and the feasibility to use Sentinel-2 spectral data to obtain valuable information on vineyards [24][25][26]. Therefore, while we are aware that using higher-resolution products would improve the results, the findings of this study highlighted that Sentinel-2 data could support decision-making towards assessment and recovery management.…”

“…The pixel-spacing of 10 × 10 or 20 × 20 m implies the inclusion of both rows and inter-rows, affecting crop status evaluation [52]. This spatial resolution limits the employment of Sentinel-2 imagery to areas larger than 0.5 ha [26,33]. Moreover, the cloud coverage may represent a limitation to the usability of the images, especially during early spring, when weather conditions are unstable.…”

“…However, the spatial resolution of these images is not high enough to enable the discrimination between rows and inter-rows of the vineyards, and as such limit their application to this crop [24]. Previous research has highlighted remarkable consistency between vegetation indices (VIs) derived from high-and medium-spatial resolution images [24][25][26][27]. Open source medium-resolution datasets were used in vineyards to determine leaf area index (LAI) [28], predict wine yield [29,30] and assess heat stress [24].…”

Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery

“…Pixels close to the border zones in a parcel are considerably affected by the adjacent pixels belonging to the intended zone (mainly roads), reducing the estimated NDVI values. This fact becomes more important as the spatial resolution of the platform is increased and thus is much more important in the case of satellites than in the case of UAVs [69].…”

Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards

“…For the spatial variation comparison of proximal and satellite systems and their relation to the yield, four VIs, and two VBVs were examined. As far as it concerns the VIs, the most commonly used vegetation index, NDVI, and the innovative and intriguing calculations of NDRE, MSAVI, and NDWI were computed [13,33,[57][58][59].…”