Abstract
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.
Highlights
Water is an important factor limiting crop quality and yield
The other method is the statistical crop water stress index (CWSI) (CWSIs) [13], in which temperatures of wet reference (Twet) is estimated by the average of the lowest 5% of temperatures histogram [14], and Tdry is assumed to be equal to the air temperature (Tair) + 5 ◦C [15,16,17,18]
The objectives of this study are to: (i) eliminate the soil background and acquire pure canopy pixels using the Canny edge detection method and a series of image processing; (ii) obtain Twet, Tdry, and Tl from canopy temperature histograms merely based on unmanned aerial vehicle (UAV) thermal infrared imagery; (iii) establish an optimized relationship to effectively diagnose cotton water stress conditions by comparing CWSIe, CWSIs, CWSIsi, and spectral indices
Summary
Water is an important factor limiting crop quality and yield. As the global climate changes and the imbalance between water supply and demand grows, farmers are faced with great shortages in agricultural water resources, especially in the arid and semi-arid areas of northwest China [1]. Twet is fully transpiring leaves with open stomata obtained by spraying part of the canopy [10,11], and Tdry is non-transpiring leaves with closed stomata obtained by covering the leaves with petroleum jelly [10,12] These natural reference surfaces are disturbed by meteorological factors and the location of reference leaves, and CWSIe may not be uniform in different regions. The other method is the statistical CWSI (CWSIs) [13], in which Twet is estimated by the average of the lowest 5% of temperatures histogram [14], and Tdry is assumed to be equal to the air temperature (Tair) + 5 ◦C [15,16,17,18]. These literatures rarely eliminate the effect of soil background pixels nor consider the unstable effect of air temperature
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