Abstract
Appropriate characterization of intra-parcel variability is a key element for the effective application of precision farming techniques. Nowadays there are many platforms available to end users differing for pixel spatial resolution and the type of acquisition (remote or proximal). A challenging aspect pertaining to remote sensing image acquisition in the vineyard ecosystem is that, in a large majority of cases, vegetation is discontinuous and single rows alternate with strips of either bare or grassed soil. In this paper, four different satellite platforms (Sentinel-2, Spot-6, Pleiades, and WorldView-3) having different spatial resolution and MECS-VINE® proximity sensor were compared in terms of accuracy at describing spatial variability. Vineyard mapping was coupled with detailed ground truthing of growth, yield, and grape composition variables. The analysis was conducted based on vigor indices (Normalized Difference Vegetation Index or Canopy Index) and using the Moran Index (MI) to assess the degree of spatial auto-correlation for the different variables. The results obtained showed a large degree of intra-plot variability in the main agronomic parameters (pruning weight CV: 33.86%, yield: 32.09%). The univariate Moran index showed a log-linear function relating MI coefficients to the resolution levels. Comparison between vigor indices and agronomic data showed that the highest bivariate MI was reached by Pleiades followed by MECS-VINE® which also did not exhibit the negative effect of the border pixel owing to the proximal scanning acquisition. Despite WorldView-3′s high resolution (1.24 m pixel) allowing very detailed data imaging, the comparison with ground-truth data was not encouraging, probably due to the presence of pure ground pixels, while Sentinel-2 was affected by the oversized pixel at 10 m.
Highlights
The core of precision agriculture (PA) application takes in-field variability into account [1,2,3]
The 2016 season was quite representative of the average climate trend of the area with growing degree days (GDD) calculated from 1 April to 30 September setting at
Similar variability was assessed for the other indicator of vine capacity, total leaf area, which varied between 1.73 m2 and 6.77 m2 per vine
Summary
The core of precision agriculture (PA) application takes in-field variability into account [1,2,3]. Its characterization is left to spatial and temporal mapping of crop status, vegetative growth, yield, and fruit quality parameters and paves the way to the enticing perspective that the general negative traits usually bound to “variability”, might turn into an unexpectedly profitable scenario [4]. Difficulties and opportunities related to a PA approach might drastically change depending upon having, for instance, a field crop forming a continuous green cover or an orchard system typically featuring discontinuous vegetation where rows alternate to soil strips. It is not surprising if a very high number of PA applications pertain to the vineyard ecosystem [10,11,12]
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