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Remote Sens. 2017, 9(8), 828; doi:10.3390/rs9080828

Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

1
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
2
School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
3
Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3168, Australia
4
Research Group on Plant Biology under Mediterranean Conditions, Department of Biology, UIB-Universitat de les Illes Balears, Carretera de Valldemossa Km 7.5, 07122 Palma de Mallorca, Spain
5
Department of Economic Development, Jobs, Transport and Resources, Tatura, VIC 3616, Australia
*
Author to whom correspondence should be addressed.
Received: 30 June 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
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Abstract

The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific (‘adaptive’ hereafter) lower and higher temperature references (Twet and Tdry respectively). Direct measurements of stem water potential (SWP, ψstem) and stomatal conductance (gs) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψstem and gs with determination coefficients (R2) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R2 of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops. View Full-Text
Keywords: thermal infrared (TIR) imagery; crop water stress index (CWSI); adaptive reference temperature thresholds; Gaussian mixture model (GMM); edge detection; stem water potential (SWP) map thermal infrared (TIR) imagery; crop water stress index (CWSI); adaptive reference temperature thresholds; Gaussian mixture model (GMM); edge detection; stem water potential (SWP) map
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Park, S.; Ryu, D.; Fuentes, S.; Chung, H.; Hernández-Montes, E.; O’Connell, M. Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote Sens. 2017, 9, 828.

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