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GRAPE VARIETY IDENTIFICATION AND DETECTION OF TERROIR parcels the size of typical vineyards (some hectares). The relatively large availability of orbital EFFECTS FROM SATELLITE IMAGES data now makes possible to apply Remote sensing techniques to viticulture. This study reports an investigation on spectral behavior of some grape varieties distributed worldwide, trying to characterize their spectral differences, and looking for differences due to terroir effects. G. Cemin(1), J. R. Ducati(2) MATERIALS AND METHODS (1)Instituto de Saneamento Ambiental Satellite images from the ASTER sensor aboard Terra satellite, all class L1B, were used. Regions Universidade de Caxias do Sul selected were in Bordeaux, Champagne, Aconcagua and Colchagua (Chile) and Encruzilhada Rua Francisco Getúlio Vargas 1130, CEP 95070-560, Caxias do Sul, Brazil (Brazil).A description of ASTER can be found at Abrams et al. (2002). Several ASTER images [email protected] (2)Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia for each region were selected, in an effort to cover the period of the phenological cycle of vines Universidade Federal do Rio Grande do Sul were reflectance is dominated by leaves. Image dates are given in Tab. 1. Av. Bento Goncalves 9500, CEP 91501-970, Porto Alegre, Brazil [email protected] Table 1. ASTER satellite images used. Places, dates, and grape varieties are indicated.

Number of pixels Terroir Image dates Cabernet Merlot Pinot Noir Chardonnay Sauvignon ABSTRACT Château 07/24/2001 Satellite images are used to determine the reflectance dependency to wavelength in different Giscours 08/11/2001 68 60 ------grape varieties (Cabernet Sauvignon, Merlot, Pinot Noir, and Chardonnay). The terroir influence 08/26/2007 is investigated through study of vineyards in France, Brazil and Chile. Statistical techniques Château 07/24/2001 (ANOVA, cluster and discriminant analysis) are applied. Results indicate that there are consistent Duhart Milon 08/11/2001 80 70 ------spectral features, mainly in the near infrared, which can lead to variety identification. These 08/26/2007 features are affected by terroir effects, since the reflectance spectra showed similarities between 11/01/2004 Encruzilhada regions, especially for Cabernet Sauvignon; phenological factors further contribute to variety 11/17/2004 50 41 36 50 do Sul differentiation. An additional search of terroir effects is made on some plots of Sangiovese, 03/06/2004 located in Tuscany and south Brazil; in this case, differences in spectral features are more 12/12/2000 Colchagua important, suggesting that clonal differences may also play a role. It is concluded that remote 01/29/2001 100 ------100 Valley sensing data are effective to terroir and grape variety studies. 02/24/2002 12/12/2000 Aconcagua KEYWORD 02/08/2002 50 50 ------Valley remote sensing – satellite images – spectral features 02/24/20002 06/08/2006 Champagne 07/17/2006 ------207 198 INTRODUCTION 09/06/2004 Satellite images have been used to study the spectral response of vegetation, including grape vines. Multispectral images provide important information in the visible and infrared, and their The grape varieties chosen for this study were Cabernet Sauvignon, Merlot, Sangiovese, Pinot potential to identify grape varieties has been demonstrated, based on the fact that peculiarities in Noir, and Chardonnay. Identification of vine parcels in satellite images was made possible with leaf characteristics in different varieties are transmitted to each reflectance spectrum (Silva and help of maps of properties, kindly given by owners (Fig. 1). Locations of vineyards, some Ducati 2009). However, terroir effects can have their influence in the spectral response of plants, thousands of kilometres apart, ensure that characteristic spectral features for a given variety, if and grapevines are especially sensitive to changes of soil, climate, and management. The they exist, come from the variety and not from the terroir; however, second-order effects, due to detection of these effects in the field can be greatly helped by the use of Remote Sensing, since terroir, can be looked for. Spectra for each grape variety, in each satellite image, were made from present-day satellites include imagers which have spatial resolution good enough to show land the mean value of reflectance, calculated for the pixels located inside the selected vine parcels. This was made for all nine spectral bands of ASTER in visible light and infrared.

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GRAPE VARIETY IDENTIFICATION AND DETECTION OF TERROIR parcels the size of typical vineyards (some hectares). The relatively large availability of orbital EFFECTS FROM SATELLITE IMAGES data now makes possible to apply Remote sensing techniques to viticulture. This study reports an investigation on spectral behavior of some grape varieties distributed worldwide, trying to characterize their spectral differences, and looking for differences due to terroir effects. G. Cemin(1), J. R. Ducati(2) MATERIALS AND METHODS (1)Instituto de Saneamento Ambiental Satellite images from the ASTER sensor aboard Terra satellite, all class L1B, were used. Regions Universidade de Caxias do Sul selected were in Bordeaux, Champagne, Aconcagua and Colchagua (Chile) and Encruzilhada Rua Francisco Getúlio Vargas 1130, CEP 95070-560, Caxias do Sul, Brazil (Brazil).A description of ASTER can be found at Abrams et al. (2002). Several ASTER images [email protected] (2)Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia for each region were selected, in an effort to cover the period of the phenological cycle of vines Universidade Federal do Rio Grande do Sul were reflectance is dominated by leaves. Image dates are given in Tab. 1. Av. Bento Goncalves 9500, CEP 91501-970, Porto Alegre, Brazil [email protected] Table 1. ASTER satellite images used. Places, dates, and grape varieties are indicated.

Number of pixels Terroir Image dates Cabernet Merlot Pinot Noir Chardonnay Sauvignon ABSTRACT Château 07/24/2001 Satellite images are used to determine the reflectance dependency to wavelength in different Giscours 08/11/2001 68 60 ------grape varieties (Cabernet Sauvignon, Merlot, Pinot Noir, and Chardonnay). The terroir influence 08/26/2007 is investigated through study of vineyards in France, Brazil and Chile. Statistical techniques Château 07/24/2001 (ANOVA, cluster and discriminant analysis) are applied. Results indicate that there are consistent Duhart Milon 08/11/2001 80 70 ------spectral features, mainly in the near infrared, which can lead to variety identification. These 08/26/2007 features are affected by terroir effects, since the reflectance spectra showed similarities between 11/01/2004 Encruzilhada regions, especially for Cabernet Sauvignon; phenological factors further contribute to variety 11/17/2004 50 41 36 50 do Sul differentiation. An additional search of terroir effects is made on some plots of Sangiovese, 03/06/2004 located in Tuscany and south Brazil; in this case, differences in spectral features are more 12/12/2000 Colchagua important, suggesting that clonal differences may also play a role. It is concluded that remote 01/29/2001 100 ------100 Valley sensing data are effective to terroir and grape variety studies. 02/24/2002 12/12/2000 Aconcagua KEYWORD 02/08/2002 50 50 ------Valley remote sensing – satellite images – spectral features 02/24/20002 06/08/2006 Champagne 07/17/2006 ------207 198 INTRODUCTION 09/06/2004 Satellite images have been used to study the spectral response of vegetation, including grape vines. Multispectral images provide important information in the visible and infrared, and their The grape varieties chosen for this study were Cabernet Sauvignon, Merlot, Sangiovese, Pinot potential to identify grape varieties has been demonstrated, based on the fact that peculiarities in Noir, and Chardonnay. Identification of vine parcels in satellite images was made possible with leaf characteristics in different varieties are transmitted to each reflectance spectrum (Silva and help of maps of properties, kindly given by owners (Fig. 1). Locations of vineyards, some Ducati 2009). However, terroir effects can have their influence in the spectral response of plants, thousands of kilometres apart, ensure that characteristic spectral features for a given variety, if and grapevines are especially sensitive to changes of soil, climate, and management. The they exist, come from the variety and not from the terroir; however, second-order effects, due to detection of these effects in the field can be greatly helped by the use of Remote Sensing, since terroir, can be looked for. Spectra for each grape variety, in each satellite image, were made from present-day satellites include imagers which have spatial resolution good enough to show land the mean value of reflectance, calculated for the pixels located inside the selected vine parcels. This was made for all nine spectral bands of ASTER in visible light and infrared.

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Cabernet Sauvignon and Merlot display similar spectra; this similarity is more striking, if they are compared with spectra of Pinot Noir and Chardonnay (Fig. 3), which also have similarities. Main differences are around the peak at 0.807 m (band 3), which, in Pinot Noir and Chardonnay, is higher with respect to values at bands 2 and 4 than at Cabernet and Merlot. One notes that reflectances from the Brazilian estate are always the highest; however, both images were acquired at the earliest stage of the vegetative cycle, compared with images of other sites, being separated by only one satellite revisit period (16 days), since other passage dates were not available. Regarding image dates, analyzing all four set of spectra in Figs. 2 and 3 it is possible to detect a tendency to lower reflectances as the season progresses. These characteristic features are present at all terroirs, showing that spectral tracings for each grape variety are maintained, even under wide environmental changes.

45 45

40 40 Champagne-06/08/2006 Champagne-06/08/2006 35 Champagne-07/17/2006 35 Champagne-07/17/2006 Champagne-09/06/2004 30 30 Champagne-09/06/2004 Colchagua-12/12/2000 25 25 Encruzilhada do Sul-11/01/2004 Colchagua-01/29/2001 20 20 Colchagua-02/24/2002 Encruzilhada do Sul-11/17/2004 15 Encruzilhada do Sul-11/01/2004 Encruzilhada do Sul-03/06/2004 15 Encruzilhada do Sul-11/17/2004 Mean reflectance (%) Mean reflectance 10 (%) reflectance Mean 10 Encruzilhada do Sul-03/06/2004 5 5 0 0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Fig. 1. Identification of vine parcels in ASTER satellite image, Chile Band Band Fig. 3. As in Fig. 2, for Pinot Noir (left) and Chardonnay (right). The statistical techniques t-test, besides variance, cluster, and discriminant analysis were applied, supported by data on Vegetation Index (NDVI). Statistical analysis of NDVI values The spectral data shown in Figs. 2 and 3 was used in cluster analysis. As an example, one of the defined centroids which were used to derive discriminant functions for each region, indicating the resulting dendrograms is shown in Fig. 4 for Cabernet Sauvignon and Chardonnay. Analysis combinations of spectral bands that optimize variety separation. show that, for these and also the other grape varieties, separately and as the primary reference, the phase of phenological cycle appears as the first separator: spring images in most cases have RESULTS AND DISCUSSION higher reflectances, a fact which can be correlated with the observation by Patakas et al (1997) Cabernet Sauvignon and Merlot display similar spectra (Fig. 2); this similarity is more striking, if that mature grape leaves are prone to contain more water. The second separator is the region, and compared with spectra of Pinot Noir and Chardonnay, which also have similarities. These there, it is also possible to observe a trend to region grouping. characteristic features are present at all terroirs, showing that spectral tracings for each grape variety are maintained, even under wide environmental changes.

Aconcagua-12/12/2000 40 40 Aconcagua-12/12/2000 Aconcagua-02/08/2002 35 Aconcagua-08/02/2002 Aconcagua-02/24/2002 35

Colchagua-12/12/2000 Aconcagua-24/02/2002 30 30 Colchagua-01/29/2001 C. Giscours-07/24/2001 25 Colchagua-02/24/2002 25 C. Giscours-08/11/2001

20 C. Giscours-07/24/2001 C. Giscours-08/26/2007 20 C. Giscours-08/11/2001 C.Duhart Milon-07/24/2001 15 C. Giscours-08/26/2007 15 C.Duhart Milon-08/11/2001

Mean reflectanceMean (%) 10 C.Duhart M ilon-07/24/2001

Mean reflectance (%) reflectance Mean C.Duhart Milon-08/26/2007 10 C.Duhart M ilon-08/11/2001 5 Encruzilhada do Sul-11/01/2004 C.Duhart M ilon-08/26/2007 5 Encruzilhada do Sul-11/17/2004 0 Encruzilhada do Sul-11/01/2004 Fig. 4. Dendrograms for Cabernet Sauvignon and Chardonnay. Regions and dates are shown. 1 2 3 4 5 6 7 8 9 0 Encruzilhada do Sul-03/06/2004 Encruzilhada do Sul-11/17/2004 1 2 3 4 5 6 7 8 9 Band Encruzilhada do Sul-03/06/2004 Band Fig. 2. Reflectance spectra for Cabernet Sauvignon (left) and Merlot (right), produced from Image classifications for each region, done after their respective discriminant functions, showed ASTER satellite images for several regions and dates. grape varieties identification and separations with accuracies ranging from 65% to nearly 100%. This is shown in Fig. 5 for a region in Bordeaux, where Cabernet Sauvignon and Merlot are well separated.

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Cabernet Sauvignon and Merlot display similar spectra; this similarity is more striking, if they are compared with spectra of Pinot Noir and Chardonnay (Fig. 3), which also have similarities. Main differences are around the peak at 0.807 m (band 3), which, in Pinot Noir and Chardonnay, is higher with respect to values at bands 2 and 4 than at Cabernet and Merlot. One notes that reflectances from the Brazilian estate are always the highest; however, both images were acquired at the earliest stage of the vegetative cycle, compared with images of other sites, being separated by only one satellite revisit period (16 days), since other passage dates were not available. Regarding image dates, analyzing all four set of spectra in Figs. 2 and 3 it is possible to detect a tendency to lower reflectances as the season progresses. These characteristic features are present at all terroirs, showing that spectral tracings for each grape variety are maintained, even under wide environmental changes.

45 45

40 40 Champagne-06/08/2006 Champagne-06/08/2006 35 Champagne-07/17/2006 35 Champagne-07/17/2006 Champagne-09/06/2004 30 30 Champagne-09/06/2004 Colchagua-12/12/2000 25 25 Encruzilhada do Sul-11/01/2004 Colchagua-01/29/2001 20 20 Colchagua-02/24/2002 Encruzilhada do Sul-11/17/2004 15 Encruzilhada do Sul-11/01/2004 Encruzilhada do Sul-03/06/2004 15 Encruzilhada do Sul-11/17/2004 Mean reflectance (%) Mean reflectance 10 (%) reflectance Mean 10 Encruzilhada do Sul-03/06/2004 5 5 0 0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Fig. 1. Identification of vine parcels in ASTER satellite image, Chile Band Band Fig. 3. As in Fig. 2, for Pinot Noir (left) and Chardonnay (right). The statistical techniques t-test, besides variance, cluster, and discriminant analysis were applied, supported by data on Vegetation Index (NDVI). Statistical analysis of NDVI values The spectral data shown in Figs. 2 and 3 was used in cluster analysis. As an example, one of the defined centroids which were used to derive discriminant functions for each region, indicating the resulting dendrograms is shown in Fig. 4 for Cabernet Sauvignon and Chardonnay. Analysis combinations of spectral bands that optimize variety separation. show that, for these and also the other grape varieties, separately and as the primary reference, the phase of phenological cycle appears as the first separator: spring images in most cases have RESULTS AND DISCUSSION higher reflectances, a fact which can be correlated with the observation by Patakas et al (1997) Cabernet Sauvignon and Merlot display similar spectra (Fig. 2); this similarity is more striking, if that mature grape leaves are prone to contain more water. The second separator is the region, and compared with spectra of Pinot Noir and Chardonnay, which also have similarities. These there, it is also possible to observe a trend to region grouping. characteristic features are present at all terroirs, showing that spectral tracings for each grape variety are maintained, even under wide environmental changes.

Aconcagua-12/12/2000 40 40 Aconcagua-12/12/2000 Aconcagua-02/08/2002 35 Aconcagua-08/02/2002 Aconcagua-02/24/2002 35

Colchagua-12/12/2000 Aconcagua-24/02/2002 30 30 Colchagua-01/29/2001 C. Giscours-07/24/2001 25 Colchagua-02/24/2002 25 C. Giscours-08/11/2001

20 C. Giscours-07/24/2001 C. Giscours-08/26/2007 20 C. Giscours-08/11/2001 C.Duhart Milon-07/24/2001 15 C. Giscours-08/26/2007 15 C.Duhart Milon-08/11/2001

Mean reflectanceMean (%) 10 C.Duhart M ilon-07/24/2001

Mean reflectance (%) reflectance Mean C.Duhart Milon-08/26/2007 10 C.Duhart M ilon-08/11/2001 5 Encruzilhada do Sul-11/01/2004 C.Duhart M ilon-08/26/2007 5 Encruzilhada do Sul-11/17/2004 0 Encruzilhada do Sul-11/01/2004 Fig. 4. Dendrograms for Cabernet Sauvignon and Chardonnay. Regions and dates are shown. 1 2 3 4 5 6 7 8 9 0 Encruzilhada do Sul-03/06/2004 Encruzilhada do Sul-11/17/2004 1 2 3 4 5 6 7 8 9 Band Encruzilhada do Sul-03/06/2004 Band Fig. 2. Reflectance spectra for Cabernet Sauvignon (left) and Merlot (right), produced from Image classifications for each region, done after their respective discriminant functions, showed ASTER satellite images for several regions and dates. grape varieties identification and separations with accuracies ranging from 65% to nearly 100%. This is shown in Fig. 5 for a region in Bordeaux, where Cabernet Sauvignon and Merlot are well separated.

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studies will use, in South America, younger vineyards in other regions. It seems possible to develop methods and techniques to differentiate grape varieties, especially when sensors and data in the infrared are available. Specifically, remote sensing techniques are sensitive to changes of reflectance through the spectral domain of ASTER sensor, and applications to studies of differences between grape varieties do turns significant results, and stimulate further research in this relatively unexplored field.

ACKNOWLEDGMENTS The authors are grateful to the owners and managers of Viña Errazuriz and Viña Viu Manent, in Chile; Vinhos Lídio Carraro, in Brazil; Chateau Giscours, Chateau Duhart Milon, and Champagne Roederer, in France, and to a owner in Montalcino, for kindly permit to use data on their properties. ASTER data was freely available through a project approved by NASA.

BIBLIOGRAPHY Patakas, A., Noitsakis, B., Stavrakas, D. 1997. Adaptation of leaves of *Vitis vinifera* L. to Fig. 5. Separation between Cabernet Sauvignon and Merlot in a region of Bordeaux, made from a seasonal drought as affected by leaf age. Vitis 36 (1) 11-14. discriminant function derived for the area. Silva, P. R., Ducati, J.R. 2009. Spectral features of vineyards in south Brazil from ASTER imaging. International Journal of Remote Sensing 30 (23), 6085-6098. Results for Sangiovese are shown in Fig. 6. Here, data from south Brazil and Tuscany are compared. In Brazil, two properties were studied in two images, and the property in Italy was divided in its nine parcels. Compared with results for all other varieties, results for Sangiovese show important differences in spectra.

0.4 0.4 Lote 1 - 11 0.35 0.35 Lote 2 - 13 0.3 0.3 Lote 3 - 10 0.25 0.25 Lote 1 - 10 pixels - 11/24/04 Lote 4 - 35 Lote 2 - 3 pixels - 11/24/04 0.2 0.2 Lote 5 - 14 Lote 1 - 10 pixels - 03/06/04 Lote 6 - 12 0.15 Lote 2 - 3 pixels - 03/06/04 0.15 Lote 7 - 19 0.1 0.1 Lote 8 - 9 Reflectance Normalized ReflectanceNormalized 0.05 0.05 Lote 9 - 9 0 0 0.556 0.661 0.807 1.656 2.167 2.209 2.262 2.336 2.4 0.556 0.661 0.807 1.656 2.167 2.209 2.262 2.336 2.4 Wavelenght ( m)  Wavelength (m)

Fig. 6. Reflectance spectra for ASTER images, for Brazil (left) and Tuscany (right). Differences are evident, suggesting factors as clone or sanitary state.

These results strongly suggest that it is possible to separate grape varieties, from data in satellite images, using their characteristic spectra. For this, information in the infrared is important.

CONCLUSIONS The spectral features of Cabernet Sauvignon, Merlot, Pinot Noir, and Chardonnay are maintained through territorial change and over time. This points strongly in favor of grape varieties having characteristic spectra, a conclusion supported by the consistent results for NDVI. The case of Sangiovese is more complicated, since important spectral differences were detected; future

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studies will use, in South America, younger vineyards in other regions. It seems possible to develop methods and techniques to differentiate grape varieties, especially when sensors and data in the infrared are available. Specifically, remote sensing techniques are sensitive to changes of reflectance through the spectral domain of ASTER sensor, and applications to studies of differences between grape varieties do turns significant results, and stimulate further research in this relatively unexplored field.

ACKNOWLEDGMENTS The authors are grateful to the owners and managers of Viña Errazuriz and Viña Viu Manent, in Chile; Vinhos Lídio Carraro, in Brazil; Chateau Giscours, Chateau Duhart Milon, and Champagne Roederer, in France, and to a owner in Montalcino, for kindly permit to use data on their properties. ASTER data was freely available through a project approved by NASA.

BIBLIOGRAPHY Patakas, A., Noitsakis, B., Stavrakas, D. 1997. Adaptation of leaves of *Vitis vinifera* L. to Fig. 5. Separation between Cabernet Sauvignon and Merlot in a region of Bordeaux, made from a seasonal drought as affected by leaf age. Vitis 36 (1) 11-14. discriminant function derived for the area. Silva, P. R., Ducati, J.R. 2009. Spectral features of vineyards in south Brazil from ASTER imaging. International Journal of Remote Sensing 30 (23), 6085-6098. Results for Sangiovese are shown in Fig. 6. Here, data from south Brazil and Tuscany are compared. In Brazil, two properties were studied in two images, and the property in Italy was divided in its nine parcels. Compared with results for all other varieties, results for Sangiovese show important differences in spectra.

0.4 0.4 Lote 1 - 11 0.35 0.35 Lote 2 - 13 0.3 0.3 Lote 3 - 10 0.25 0.25 Lote 1 - 10 pixels - 11/24/04 Lote 4 - 35 Lote 2 - 3 pixels - 11/24/04 0.2 0.2 Lote 5 - 14 Lote 1 - 10 pixels - 03/06/04 Lote 6 - 12 0.15 Lote 2 - 3 pixels - 03/06/04 0.15 Lote 7 - 19 0.1 0.1 Lote 8 - 9 Reflectance Normalized ReflectanceNormalized 0.05 0.05 Lote 9 - 9 0 0 0.556 0.661 0.807 1.656 2.167 2.209 2.262 2.336 2.4 0.556 0.661 0.807 1.656 2.167 2.209 2.262 2.336 2.4 Wavelenght ( m)  Wavelength (m)

Fig. 6. Reflectance spectra for ASTER images, for Brazil (left) and Tuscany (right). Differences are evident, suggesting factors as clone or sanitary state.

These results strongly suggest that it is possible to separate grape varieties, from data in satellite images, using their characteristic spectra. For this, information in the infrared is important.

CONCLUSIONS The spectral features of Cabernet Sauvignon, Merlot, Pinot Noir, and Chardonnay are maintained through territorial change and over time. This points strongly in favor of grape varieties having characteristic spectra, a conclusion supported by the consistent results for NDVI. The case of Sangiovese is more complicated, since important spectral differences were detected; future

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