IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i5p457-d557003.html
   My bibliography  Save this article

Remote Sensing Vegetation Indices in Viticulture: A Critical Review

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/5/457/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/5/457/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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).
    6. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. Boninsenha, Ígor & Mantovani, Everardo C. & Rudnick, Daran R. & Ribeiro, Higor de Q., 2024. "Revealing irrigation uniformity with remote sensing: A comparative analysis of satellite-derived uniformity coefficient," Agricultural Water Management, Elsevier, vol. 301(C).
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bretreger, David & Yeo, In-Young & Hancock, Greg, 2022. "Quantifying irrigation water use with remote sensing: Soil water deficit modelling with uncertain soil parameters," Agricultural Water Management, Elsevier, vol. 260(C).
    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. 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.
    4. Araneda-Cabrera, Ronnie J. & Bermúdez, María & Puertas, Jerónimo, 2021. "Assessment of the performance of drought indices for explaining crop yield variability at the national scale: Methodological framework and application to Mozambique," Agricultural Water Management, Elsevier, vol. 246(C).
    5. Zhang, Yu & Hao, Zengchao & Feng, Sifang & Zhang, Xuan & Hao, Fanghua, 2022. "Changes and driving factors of compound agricultural droughts and hot events in eastern China," Agricultural Water Management, Elsevier, vol. 263(C).
    6. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2021. "The mean value of gaussian distribution of excess green index: A new crop water stress indicator," Agricultural Water Management, Elsevier, vol. 251(C).
    7. Pardo, J.J. & Domínguez, A. & Léllis, B.C. & Montoya, F. & Tarjuelo, J.M. & Martínez-Romero, A., 2022. "Effect of the optimized regulated deficit irrigation methodology on quality, profitability and sustainability of barley in water scarce areas," Agricultural Water Management, Elsevier, vol. 266(C).
    8. Soumyashree Dixit & V. Neethin & K. V. Jayakumar, 2023. "Assessment of Crop-Drought Relationship: A Climate Change Perspective," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 4075-4095, August.
    9. Manman Zhang & Dang Luo & Yongqiang Su, 2022. "Drought monitoring and agricultural drought loss risk assessment based on multisource information fusion," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 775-801, March.
    10. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.
    11. Rutger Willem Vervoort & Ignacio Fuentes & Joost Brombacher & Jelle Degen & Pedro Chambel-Leitão & Flávio Santos, 2022. "Progress in Developing Scale-Able Approaches to Field-Scale Water Accounting Based on Remote Sensing," Sustainability, MDPI, vol. 14(5), pages 1-22, February.
    12. Clara Gabaldón-Leal & Álvaro Sánchez-Virosta & Carolina Doña & José González-Piqueras & Juan Manuel Sánchez & Ramón López-Urrea, 2024. "Ground Measurements and Remote Sensing Modeling of Gross Primary Productivity and Water Use Efficiency in Almond Agroecosystems," Agriculture, MDPI, vol. 14(9), pages 1-22, September.
    13. 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.
    14. Meng, Huayue & Qian, Long, 2024. "Performances of different yield-detrending methods in assessing the impacts of agricultural drought and flooding: A case study in the middle-and-lower reach of the Yangtze River, China," Agricultural Water Management, Elsevier, vol. 296(C).
    15. Dong, Hao & Dong, Jiahui & Sun, Shikun & Bai, Ting & Zhao, Dongmei & Yin, Yali & Shen, Xin & Wang, Yakun & Zhang, Zhitao & Wang, Yubao, 2024. "Crop water stress detection based on UAV remote sensing systems," Agricultural Water Management, Elsevier, vol. 303(C).
    16. Nur Adibah Mohidem & Nik Norasma Che’Ya & Abdul Shukor Juraimi & Wan Fazilah Fazlil Ilahi & Muhammad Huzaifah Mohd Roslim & Nursyazyla Sulaiman & Mohammadmehdi Saberioon & Nisfariza Mohd Noor, 2021. "How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?," Agriculture, MDPI, vol. 11(10), pages 1-27, October.
    17. Potopová, V. & Trnka, M. & Vizina, A. & Semerádová, D. & Balek, J. & Chawdhery, M.R.A. & Musiolková, M. & Pavlík, P. & Možný, M. & Štěpánek, P. & Clothier, B., 2022. "Projection of 21st century irrigation water requirements for sensitive agricultural crop commodities across the Czech Republic," Agricultural Water Management, Elsevier, vol. 262(C).
    18. Marco Ammoniaci & Simon-Paolo Kartsiotis & Rita Perria & Paolo Storchi, 2021. "State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture," Agriculture, MDPI, vol. 11(3), pages 1-20, February.
    19. 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.
    20. Gonçalves, Ivo Zution & Mekonnen, Mesfin M. & Neale, Christopher M.U. & Campos, Isidro & Neale, Michael R., 2020. "Temporal and spatial variations of irrigation water use for commercial corn fields in Central Nebraska," Agricultural Water Management, Elsevier, vol. 228(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:11:y:2021:i:5:p:457-:d:557003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.