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High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard

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  • Santesteban, L.G.
  • Di Gennaro, S.F.
  • Herrero-Langreo, A.
  • Miranda, C.
  • Royo, J.B.
  • Matese, A.

Abstract

Thermal imaging can become a readily usable tool for crop agricultural water management, since it allows a quick determination of canopy surface temperature that, as linked to transpiration, can give an idea of crop water status. In the last years, the resolution of thermal imaging systems has increased and its weight decreased, fostering their implementation on Unmanned Aerial Vehicles (UAV) for civil and agricultural engineering purposes. This approach would overcome most of the limitations of on site thermal imaging, allowing mapping plant water status at either field or farm scale, taking thus into account the naturally existing or artificially induced variability at those scales. The aim of this work was to evaluate to which extent high-resolution thermal imaging allows evaluating the instantaneous and seasonal variability of water status within a vineyard. The novelty and significance of our approach is that the specifically designed and build unmanned aerial vehicle (UAV) provided very high-resolution imaging (pixel <9cm), and that it was used at a commercially relevant acreage (7.5ha). This set-up was used to obtain Crop Water Stress Index (CWSI) from thermal images in a clear-sky day. CWSI values were and compared to stem water potential (Ψs) and stomatal conductance (gs) measured at 14 sampling sites across the vineyard at the moment when images where acquired. In order to evaluate the potential of CWSI acquired in a single day to estimate within-vineyard patterns of variation in water status, a spatial modeling approach was used. CWSI correlated well with Ψs and gs at the moment of image acquisition, showing to have a great potential to monitor instantaneous variations in water status within a vineyard. The information provided by thermal images proved to be relevant at a seasonal scale as well, although it did not match seasonal trends in water status but mimicked other physiological processes occurring during ripening. Therefore, if a picture of variations in water status is required, it would be necessary to acquire thermal images at several dates along the summer.

Suggested Citation

  • Santesteban, L.G. & Di Gennaro, S.F. & Herrero-Langreo, A. & Miranda, C. & Royo, J.B. & Matese, A., 2017. "High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard," Agricultural Water Management, Elsevier, vol. 183(C), pages 49-59.
    • Handle: RePEc:eee:agiwat:v:183:y:2017:i:c:p:49-59
    DOI: 10.1016/j.agwat.2016.08.026
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    1. Orley Ashenfelter & Karl Storchmann, 2016. "Editor's Choice The Economics of Wine, Weather, and Climate Change," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 10(1), pages 25-46.
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    2. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.
    3. Apolo-Apolo, O.E. & Martínez-Guanter, J. & Pérez-Ruiz, M. & Egea, G., 2020. "Design and assessment of new artificial reference surfaces for real time monitoring of crop water stress index in maize," Agricultural Water Management, Elsevier, vol. 240(C).
    4. de Almeida, Ailson Maciel & Coelho, Rubens Duarte & da Silva Barros, Timóteo Herculino & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto & Moreno-Pizani, Maria Alejandra & Farias-Ram, 2022. "Water productivity and canopy thermal response of pearl millet subjected to different irrigation levels," Agricultural Water Management, Elsevier, vol. 272(C).
    5. Ramírez-Cuesta, J.M. & Ortuño, M.F. & Gonzalez-Dugo, V. & Zarco-Tejada, P.J. & Parra, M. & Rubio-Asensio, J.S. & Intrigliolo, D.S., 2022. "Assessment of peach trees water status and leaf gas exchange using on-the-ground versus airborne-based thermal imagery," Agricultural Water Management, Elsevier, vol. 267(C).
    6. Puppala, Harish & Peddinti, Pranav R.T. & Tamvada, Jagannadha Pawan & Ahuja, Jaya & Kim, Byungmin, 2023. "Barriers to the adoption of new technologies in rural areas: The case of unmanned aerial vehicles for precision agriculture in India," Technology in Society, Elsevier, vol. 74(C).
    7. 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.
    8. Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.
    9. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).

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