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.
Highlights
Precision viticulture (PV) has been suggested as an effective approach to reach the high-quality standards required for wine production (Bramley and Hamilton, 2007)
R2 was significant in all cases (p < 0.01), which is evidence of a relevant link between S2 Normalised difference vegetation index (NDVI) and all derived unmanned aerial vehicles (UAV) indices
The comparison between NDVI from Sentinel-2 and pure vine NDVI extracted from UAV images showed an increasing correlation if the border pixels were removed from the satellite’s images
Summary
Precision viticulture (PV) has been suggested as an effective approach to reach the high-quality standards required for wine production (Bramley and Hamilton, 2007). High-resolution and proximal sensors may be used in PV, lower-resolution remote-sensing applications, such as satellite imagery, have proved to be challenging due to peculiarities in vineyards such as vegetation within inter-rows or diversity of training systems. UAV and airborne sensors provide high-resolution imagery (spatial, spectral and radiometric) that can be implemented in order to extract various types of vineyard information (Pichon et al, 2019), these are expensive, limited in the area of acquisition possible, and require specialised postprocessing to achieve good final imagery (Candiago et al, 2015). Since 2018 Sentinel 2 constellation has provided images every 5 days Both satellites carry a multispectral imaging sensor (MSI) able to acquire images from 433 nm up to 2280 nm, in 13 bands. Red (665 nm) and near-infrared (842 nm) are of particular interest for agriculture application as they make it possible to retrieve several vegetation indices at 10 m of spatial resolution
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