Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard

Suyoung Park; Dongryeol Ryu; Sigfredo Fuentes; Hoam Chung; Mark O’Connell; Junchul Kim

doi:10.3390/rs13142775

Remote Sensing (Jul 2021)

Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard

Suyoung Park,
Dongryeol Ryu,
Sigfredo Fuentes,
Hoam Chung,
Mark O’Connell,
Junchul Kim

Affiliations

Suyoung Park: Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Dongryeol Ryu: Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Sigfredo Fuentes: Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
Hoam Chung: Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia
Mark O’Connell: Agriculture Victoria Research, Department of Jobs, Precincts and Regions, Tatura, VIC 3616, Australia
Junchul Kim: Center for Data Science, Seoul Institute of Technology, Seoul 03909, Korea

DOI: https://doi.org/10.3390/rs13142775
Journal volume & issue: Vol. 13, no. 14
p. 2775

Abstract

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Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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