Land is variable and vine performances reflect such variability. Precision viticulture, based on remote sensing and variable rate technologies, is a valuable tool for its description and management. Because vineyard monitoring can be accomplished through different platforms, a wide array of solutions is now available combining different spatial and spectral resolutions, revisit time and cost. Challenge is selecting the most sensitive and cost-effective solution for facing specific technical issues. The research aims at comparing the sensitivity of four different remote and proximal sensing platforms in detecting within-field vigor variability of a 'Barbera' vineyard established in the Colli Piacentini wine district (NW of Italy). A panel of 8 vines was selected for each of three vigor classes (LV = low, MV = medium and HV = high) as previously identified by a 5 m resolution vigor map referred to July 2014. In 2016, physiological, agronomical and enological parameters were measured. Remote (satellite and UAV acquisitions at 10, 6 and 5 cm resolution) and proximal images (approximately one reading per vine) acquired in July 2016 were processed by calculating vigor indices and validated according to field data. Seasonal trend of Sentinel-2 derived NDVI values was described for each vigor class. Results show LV and HV vines always demonstrated different growth, yield and ripening patterns. This variability was successfully detected by satellite imagery and close correlations between Sentinel-2 and SPOT6-derived NDVI values with vegetative, yield and grape composition parameters were found. Canopy Index provided by proximal sensor MECS-VINE® was highly sensitive to vigor variation at the single plant scale. Open source Sentinel-2 allowed separating HV and LV areas over season suggesting potential suitability for vineyard monitoring and management under the experimental conditions. More consistent results require long-term confirmation.
|Numero di pagine||8|
|Stato di pubblicazione||Pubblicato - 2020|
- Proximal sensing
- Remote sensing
- Satellite imagery