Terroir 2010 banner
IVES 9 IVES Conference Series 9 Grape variety identification and detection of terroir effects from satellite images

Grape variety identification and detection of terroir effects from satellite images

Abstract

Satellite images are used to determine the reflectance dependency to wavelength in different grape varieties (Cabernet-Sauvignon, Merlot, Pinot Noir, and Chardonnay). The terroir influence is investigated through study of vineyards in France, Brazil and Chile. Statistical techniques (ANOVA, cluster and discriminant analysis) are applied. Results indicate that there are consistent spectral features, mainly in the near infrared, which can lead to variety identification. These features are affected by terroir effects, since the reflectance spectra showed similarities between regions, especially for Cabernet Sauvignon; phenological factors further contribute to variety differentiation. An additional search of terroir effects is made on some plots of Sangiovese, located in Tuscany and south Brazil; in this case, differences in spectral features are more important, suggesting that clonal differences may also play a role. It is concluded that remote sensing data are effective to terroir and grape variety studies.

Download the PDF

DOI:

Publication date: October 8, 2020

Issue: Terroir 2010

Type: Article

Authors

G. Cemin (1), J. R. Ducati (2)

(1) Instituto de Saneamento Ambiental. Universidade de Caxias do Sul. Rua Francisco Getúlio Vargas 1130, CEP 95070-560, Caxias do Sul, Brazil
(2) Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia. Universidade Federal do Rio Grande do Sul. Av. Bento Goncalves 9500, CEP 91501-970, Porto Alegre, Brazil

Contact the author

;

Keywords

remote sensing – satellite images – spectral features

Tags

IVES Conference Series | Terroir 2010

Citation

Related articles…

Assessing bunch architecture for grapevine yield forecasting by image analysis 

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages.
Read More

Effect of elicitors and ripening moment on the phenolic composition of Monastrell

Grapevine (Vitis vinifera L.) is a globally cultivated crop and economically significant, particularly in the wine industry (Varela et al., 2024). Climate change is already affecting vineyards and is expected to worsen (Averbeck et al., 2019; Dupuis and Knoepfel, 2011).

Read More

Evaluation of Polarized Projective Mapping as a possible tool for attributing South African Chenin blanc dry wine styles

Multiple Factor Analysis (MFA) According to the Chenin blanc Association of South Africa, there are three recognized dry wine styles, Fresh and Fruity (FF), Rich and Ripe Unwooded (RRU), and Rich and Ripe Wooded (RRW), classically attributed with the help of sensory evaluation. One of the “rapid methods” has drawn our attention for the purpose of simplifying and making style attribution for large sample sets, evaluated during different sessions, more robust. Polarized Projective Mapping (PPM) is a hybrid of Projective Mapping (PM) and Polarised Sensory Positioning (PSP). It is a reference-based method in which poles
(references) are used for the evaluation of similarities and dissimilarities between samples.

Read More

The Douro region: wine and tourism

The Demarcated Douro Region (DDR) dates from 1756, when it was recognized as one of the first demarcated regions in the world. The DDR economic activities fit the terroir model and are based on wine and tourism.

Read More

Applications of a novel molecular phenology scale to align the stages of grape berry development

Phenology scales widely adopted by viticulturists (i.e., BBCH or modified E-L systems) are classification tools that describe seasonal and precisely recognized stages of fruit growth and development based on specific descriptors such as visual/physical traits or easy-to-measure compositional parameters.

Read More