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Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

Author

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  • Evangelos Anastasiou

    (Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece)

  • Athanasios Balafoutis

    (Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
    Institute for Bio-Economy & Agri-Technology, Centre of Research & Technology Hellas, Dimarchou Georgiadou 118, 38221 Volos, Greece)

  • Nikoleta Darra

    (Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece)

  • Vasileios Psiroukis

    (Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece)

  • Aikaterini Biniari

    (Faculty of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece)

  • George Xanthopoulos

    (Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece)

  • Spyros Fountas

    (Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece)

Abstract

Table grapes are a crop with high nutritional value that need to be monitored often to achieve high yield and quality. Non-destructive methods, such as satellite and proximal sensing, are widely used to estimate crop yield and quality characteristics, and spectral vegetation indices (SVIs) are commonly used to present site specific information. The aim of this study was the assessment of SVIs derived from satellite and proximal sensing at different growth stages of table grapes from veraison to harvest. The study took place in a commercial table grape vineyard ( Vitis vinifera cv. Thompson Seedless) during three successive cultivation years (2015–2017). The Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were calculated by employing satellite imagery (Landsat 8) and proximal sensing (Crop Circle ACS 470) to assess the yield and quality characteristics of table grapes. The SVIs exhibited different degrees of correlations with different measurement dates and sensing methods. Satellite-based GNDVI at harvest presented higher correlations with crop quality characteristics ( r = 0.522 for berry diameter, r = 0.537 for pH, r = 0.629 for berry deformation) compared with NDVI. Proximal-based GNDVI at the middle of veraison presented higher correlations compared with NDVI ( r = −0.682 for berry diameter, r = −0.565 for berry deformation). Proximal sensing proved to be more accurate in terms of table grape yield and quality characteristics compared to satellite sensing.

Suggested Citation

  • Evangelos Anastasiou & Athanasios Balafoutis & Nikoleta Darra & Vasileios Psiroukis & Aikaterini Biniari & George Xanthopoulos & Spyros Fountas, 2018. "Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes," Agriculture, MDPI, vol. 8(7), pages 1-17, June.
    • Handle: RePEc:gam:jagris:v:8:y:2018:i:7:p:94-:d:154564
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    References listed on IDEAS

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    1. Er-Raki, S. & Rodriguez, J.C. & Garatuza-Payan, J. & Watts, C.J. & Chehbouni, A., 2013. "Determination of crop evapotranspiration of table grapes in a semi-arid region of Northwest Mexico using multi-spectral vegetation index," Agricultural Water Management, Elsevier, vol. 122(C), pages 12-19.
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    Cited by:

    1. Sergio Vélez & Enrique Barajas & Pilar Blanco & José Antonio Rubio & David Castrillo, 2021. "Spatio-Temporal Analysis of Satellite Imagery (NDVI) to Identify Terroir and Vineyard Yeast Differences according to Appellation of Origin (AOP) and Biogeographic Origin," J, MDPI, vol. 4(3), pages 1-13, June.
    2. Yorghos Voutos & Phivos Mylonas & John Katheniotis & Anastasia Sofou, 2019. "A Survey on Intelligent Agricultural Information Handling Methodologies," Sustainability, MDPI, vol. 11(12), pages 1-23, June.
    3. Mohamad M. Awad, 2019. "Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques," Agriculture, MDPI, vol. 9(3), pages 1-13, March.
    4. Dimitrios Tassopoulos & Dionissios Kalivas & Rigas Giovos & Nestor Lougkos & Anastasia Priovolou, 2021. "Sentinel-2 Imagery Monitoring Vine Growth Related to Topography in a Protected Designation of Origin Region," Agriculture, MDPI, vol. 11(8), pages 1-20, August.
    5. Rigas Giovos & Dimitrios Tassopoulos & Dionissios Kalivas & Nestor Lougkos & Anastasia Priovolou, 2021. "Remote Sensing Vegetation Indices in Viticulture: A Critical Review," Agriculture, MDPI, vol. 11(5), pages 1-20, May.

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