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Open AccessArticle

Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery

1
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A & F University, Yangling 712100, Shaanxi, China
2
College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling 712100, Shaanxi, China
3
Institute of Water Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, Shaanxi, China
4
Department of Foreign Languages, Northwest A & F University, Yangling 712100, Shaanxi, China
5
College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 267; https://doi.org/10.3390/rs11030267
Received: 9 January 2019 / Revised: 28 January 2019 / Accepted: 28 January 2019 / Published: 29 January 2019
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (Twet), dry reference temperature (Tdry), and mean canopy temperature (Tl) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which Twet was the mean of the lowest 5% of canopy temperatures and Tdry was the air temperature (Tair) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R2 = 0.660) and tr (R2 = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management. View Full-Text
Keywords: thermal infrared; unmanned aerial vehicle (UAV); CWSI; Canny edge detection; stomatal conductance thermal infrared; unmanned aerial vehicle (UAV); CWSI; Canny edge detection; stomatal conductance
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MDPI and ACS Style

Bian, J.; Zhang, Z.; Chen, J.; Chen, H.; Cui, C.; Li, X.; Chen, S.; Fu, Q. Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery. Remote Sens. 2019, 11, 267.

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