On the relationship between some production parameters and a vegetation index in viticulture

The use and timing of many agronomical practices such as the scheduling of irrigation and harvesting are dependent on accurate vineyard sampling of qualitative and productive parameters. Crop forecasting also depends on the representativeness of vineyard samples during the whole phenological period. This manuscript summarizes the last two years of precision viticulture in Sicily (Italy); agronomic campaigns were carried out in 2012 and 2013 within the “Tenute Rapitalà” and “Donnafugata” farms. Normalized Difference Vegetation Index derived from satellite images (RapidEye) acquired at berry set, pre-veraison and ripening phenological stages (occurred at June, July and August respectively) have been related to production parameters (sugar and anthocyanins contents) at harvesting of a selected red autochthonous cultivar (Nero D’Avola). The research aims to assess how robust are prediction models based on simple linear regression analysis, in particular: 1) whether there is a suitable period for acquiring the remote sensing image to evaluate these parameters at harvesting, when their knowledge is required; 2) if these relationships are consistent between years or need to be re-calibrated; 3) the models transferability to other vineyard of the same cultivar.

[1]  Antonino Maltese,et al.  Critical analysis of the thermal inertia approach to map soil water content under sparse vegetation and changeable sky conditions , 2012, Remote Sensing.

[2]  R. E. Smart,et al.  Sunlight into wine: a handbook for winegrape canopy management. , 1991 .

[3]  E. Vermote,et al.  Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces. , 2007, Applied optics.

[4]  R. Bramley,et al.  Understanding variability in winegrape production systems , 2004 .

[5]  G. F. Epema,et al.  Determination of planetary reflectance for Landsat-5 thematic-mapper tapes processed by Earthnet (Italy). , 1990 .

[6]  Francesco Mattia,et al.  On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Russell P. Smithyman,et al.  The Use of Competition for Carbohydrates Among Vegetative and Reproductive Sinks to Reduce Fruit Set and Botrytis Bunch Rot in Seyval blanc Grapevines , 1998, American Journal of Enology and Viticulture.

[8]  Yousef A. Al-Rumkhani,et al.  Use of remote sensing for irrigation scheduling in arid lands of Saudi Arabia , 2004 .

[9]  E. Vermote,et al.  Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance. , 2006, Applied optics.

[10]  P. Bates,et al.  Critical analysis of thermal inertia approaches for surface soil water content retrieval , 2013 .

[11]  Paul A. Henschke,et al.  Implications of nitrogen nutrition for grapes, fermentation and wine , 2005 .

[12]  Nick K. Dokoozlian,et al.  Bud Microclimate and Fruitfulness in Vitis vinifera L. , 2005, American Journal of Enology and Viticulture.

[13]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[14]  P. Dosso,et al.  Viticoltura di precisione grande risorsa per il futuro , 2006 .

[15]  E. Barlow,et al.  Development of inflorescence primordia in Vitis vinifera L. cv. Chardonnay from hot and cool climates , 2008 .

[16]  G. La Loggia,et al.  Coupling two radar backscattering models to assess soil roughness and surface water content at farm scale , 2013 .

[17]  David W. Lamb,et al.  Within-season temporal variation in correlations between vineyard canopy and winegrape composition and yield , 2011, Precision Agriculture.

[18]  D. Lamb The use of qualitative airborne multispectral imaging for managing agricultural crops : a case study in south-eastern australia , 2000 .

[19]  Dario Papale,et al.  Airborne remote sensing in precision viticolture: assessment of quality and quantity vineyard production using multispectral imagery: a case study in Velletri, Rome surroundings (central Italy) , 2009, Remote Sensing.

[20]  Luis G. Santesteban,et al.  Water status, leaf area and fruit load influence on berry weight and sugar accumulation of cv. 'Tempranillo' under semiarid conditions , 2006 .

[21]  Antonino Maltese,et al.  Mapping evapotranspiration on vineyards: a comparison between Penman-Monteith and energy balance approaches for operational purposes , 2012, Remote Sensing.

[22]  A. Moing,et al.  Grape berry development: a review , 2002 .

[23]  Antonino Maltese,et al.  Surface soil humidity retrieval using remote sensing techniques: a triangle method validation , 2010, Remote Sensing.

[24]  G. La Loggia,et al.  Surface soil humidity retrieval by means of a semi-empirical coupled SAR model , 2010, Remote Sensing.

[25]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[26]  M. Minacapilli,et al.  Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment , 2012 .

[27]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[28]  Hervé Sinoquet,et al.  Indices of light microclimate and canopy structure of grapevines determined by 3D digitising and image analysis, and their relationship to grape quality , 1998 .

[29]  D. Lamb,et al.  Precision viticulture - an Australian perspective , 2004 .

[30]  D. Lamb,et al.  Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolution , 2008 .

[31]  Antonino Maltese,et al.  Comparing actual evapotranspiration and plant water potential on a vineyard , 2011, Remote Sensing.