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Remote Sensing Vegetation Indices in Viticulture: A Critical Review

Author

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  • Rigas Giovos

    (GIS Research Unit, Laboratory of Soils and Agricultural Chemistry, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 118 55 Athens, Greece)

  • Dimitrios Tassopoulos

    (GIS Research Unit, Laboratory of Soils and Agricultural Chemistry, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 118 55 Athens, Greece)

  • Dionissios Kalivas

    (GIS Research Unit, Laboratory of Soils and Agricultural Chemistry, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 118 55 Athens, Greece)

  • Nestor Lougkos

    (GIS Research Unit, Laboratory of Soils and Agricultural Chemistry, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 118 55 Athens, Greece)

  • Anastasia Priovolou

    (GIS Research Unit, Laboratory of Soils and Agricultural Chemistry, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 118 55 Athens, Greece)

Abstract

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:5:p:457-:d:557003
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    References listed on IDEAS

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    1. Alessandro Matese & Salvatore Filippo Di Gennaro, 2018. "Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture," Agriculture, MDPI, vol. 8(7), pages 1-13, July.
    2. Bretreger, David & Yeo, In-Young & Quijano, Juan & Awad, John & Hancock, Greg & Willgoose, Garry, 2019. "Monitoring irrigation water use over paddock scales using climate data and landsat observations," Agricultural Water Management, Elsevier, vol. 221(C), pages 175-191.
    3. 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.
    4. Francisco Manuel Jiménez-Brenes & Francisca López-Granados & Jorge Torres-Sánchez & José Manuel Peña & Pilar Ramírez & Isabel Luisa Castillejo-González & Ana Isabel de Castro, 2019. "Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-21, June.
    5. Garrido-Rubio, Jesús & González-Piqueras, Jose & Campos, Isidro & Osann, Anna & González-Gómez, Laura & Calera, Alfonso, 2020. "Remote sensing–based soil water balance for irrigation water accounting at plot and water user association management scale," Agricultural Water Management, Elsevier, vol. 238(C).
    6. Potopová, Vera & Trnka, Miroslav & Hamouz, Pavel & Soukup, Josef & Castraveț, Tudor, 2020. "Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe," Agricultural Water Management, Elsevier, vol. 236(C).
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    1. Dorijan Radočaj & Ante Šiljeg & Rajko Marinović & Mladen Jurišić, 2023. "State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review," Agriculture, MDPI, vol. 13(3), pages 1-16, March.
    2. 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.
    3. Arifou Kombate & Fousseni Folega & Wouyo Atakpama & Marra Dourma & Kperkouma Wala & Kalifa Goïta, 2022. "Characterization of Land-Cover Changes and Forest-Cover Dynamics in Togo between 1985 and 2020 from Landsat Images Using Google Earth Engine," Land, MDPI, vol. 11(11), pages 1-31, October.
    4. Marek Bednář & Bořivoj Šarapatka & Patrik Netopil & Miroslav Zeidler & Tomáš Hanousek & Lucie Homolová, 2023. "The Use of Spectral Indices to Recognize Waterlogged Agricultural Land in South Moravia, Czech Republic," Agriculture, MDPI, vol. 13(2), pages 1-18, January.

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