Investigation of the similarities between NDVI maps from different proximal and remote sensing platforms in explaining vineyard variability

Abstract

Vegetation indices (VI), especially the normalised difference vegetation index (NDVI), are used to determine management units (MU) and to explain quantity and quality of vineyard production. How do NDVI maps from different sensing technologies differ in a production context? What part of the variability of yield and quality can they explain? This study compares high-resolution multispectral, multi-temporal data from CropCircle, SpectroSense + GPS, Parrot Sequoia + multispectral camera equipped UAV, and Sentinel-2 imagery over two seasons (2019 and 2020). The objective was to assess whether the date of data collection (phenological growth stage) influences the correlations between NDVI and crop production. The comparison of vineyard NDVI data from proximal and remote sensing in both a statistical and a productive context showed strong similarities between NDVI values from similar sensors (0.69 < r < 0.96), but divergences between proximal and airborne/spaceborne observations. Exploratory correlation analysis between NDVI layers and grape yield and total soluble solids data (TSS) showed high correlations (maximum |r|= 0.91 and |r|= 0.74, respectively), with correlations increasing as the season progressed. No relationship with must titratable acidity or pH was found. Finally, proximal sensors explained better the variability in yield and quality for grapes in the early and late growth stages. The UAV's MUs described the yield of both years better than the other sensors. In 2019, the PCA-based MUs explained the TSS variability better than the UAV-related zones. Due to their coarse spatial resolution, the satellite data proved inconsistent in explaining the variability.