TY - JOUR
T1 - Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV)
AU - Park, Suyoung
AU - Ryu, Dongryeol
AU - Fuentes, Sigfredo
AU - Chung, Hoam
AU - Hernández-Montes, Esther
AU - O'Connell, Mark
PY - 2017/8/1
Y1 - 2017/8/1
N2 - 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.
AB - 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.
KW - Adaptive reference temperature thresholds
KW - Crop water stress index (CWSI)
KW - Edge detection
KW - Gaussian mixture model (GMM)
KW - Stem water potential (SWP) map
KW - Thermal infrared (TIR) imagery
UR - http://www.scopus.com/inward/record.url?scp=85028331428&partnerID=8YFLogxK
U2 - 10.3390/rs9080828
DO - 10.3390/rs9080828
M3 - Article
AN - SCOPUS:85028331428
SN - 2072-4292
VL - 9
JO - Remote Sensing
JF - Remote Sensing
IS - 8
M1 - 828
ER -