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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Abiotic Stress
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1448656
This article is part of the Research Topic Abiotic and Biotic Stress Responses of Olive Trees Under Climate Change View all 3 articles

Combining proximal and remote sensing to assess 'Calatina' olive water status

Provisionally accepted
Alessandro Carella Alessandro Carella *Roberto Massenti Roberto Massenti *Francesco P. Marra Francesco P. Marra Pietro Catania Pietro Catania Eliseo Roma Eliseo Roma Riccardo Lo Bianco Riccardo Lo Bianco
  • Department of Agricultural, Food and Forestry Sciences, University of Palermo, Palermo, Sicily, Italy

The final, formatted version of the article will be published soon.

    Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R 2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

    Keywords: Precision irrigation, Drought stress, Olea europaea L., crop water stress index, Normalized Difference Vegetation Index, Stem water potential, UAVs

    Received: 13 Jun 2024; Accepted: 30 Jul 2024.

    Copyright: © 2024 Carella, Massenti, Marra, Catania, Roma and Lo Bianco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Alessandro Carella, Department of Agricultural, Food and Forestry Sciences, University of Palermo, Palermo, 90128, Sicily, Italy
    Roberto Massenti, Department of Agricultural, Food and Forestry Sciences, University of Palermo, Palermo, 90128, Sicily, Italy

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