TY - JOUR
T1 - Dependence of CWSI‐based plant water stress estimation with diurnal acquisition times in a nectarine orchard
AU - Park, Suyoung
AU - Ryu, Dongryeol
AU - Fuentes, Sigfredo
AU - Chung, Hoam
AU - O’connell, Mark
AU - Kim, Junchul
N1 - Funding Information:
The stone fruit experimental orchard project (SF12003 and SF17006) was funded by Horticulture Innovation Australia Limited using the Summerfruit levy and funds from the Australian Government with co?investment from DJPR.
Funding Information:
Funding: The stone fruit experimental orchard project (SF12003 and SF17006) was funded by Hor‐ ticulture Innovation Australia Limited using the Summerfruit levy and funds from the Australian Government with co‐investment from DJPR.
Funding Information:
Acknowledgments: This research was supported by the University of Melbourne and Department of Jobs, Precincts and Regions (DJPR), Victoria. In addition, this research was supported by the Seoul Institute of Technology (http://www.sit.re.kr) (2021‐AH‐002).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/7/14
Y1 - 2021/7/14
N2 - Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV‐borne thermogra-phy 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 determina-tion (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.
AB - Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV‐borne thermogra-phy 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 determina-tion (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.
KW - Adaptive crop water stress index (Adaptive CWSI)
KW - Remote sensing
KW - Thermal infrared (TIR) imagery
KW - Unmanned aerial vehicle (UAV)
UR - http://www.scopus.com/inward/record.url?scp=85111138830&partnerID=8YFLogxK
U2 - 10.3390/rs13142775
DO - 10.3390/rs13142775
M3 - Article
AN - SCOPUS:85111138830
SN - 2072-4292
VL - 13
JO - Remote Sensing
JF - Remote Sensing
IS - 14
M1 - 2775
ER -