Improved method of crop water stress index based on UAV remote sensing

Abstract: Canopy temperature is one of the most important parameters to calculate the crop water stress index (CWSI). Among them, the unmanned aerial vehicles (UAVs) with thermal infrared remote sensing function have been widely used to extract the crop canopy temperature in recent years. The monitoring accuracy of crop water can be improved to accurately remove the soil background in the thermal infrared images. In this study, an improved method was proposed to monitor the CWSI using UVA remote sensing. The research object was firstly selected as the summer maize at the jointing stage. Four water treatments were then carried out: T1 (50% of the field capacity), T2 (65% of the field capacity), T3 (80% of the field capacity), and T4 (95%-100% of the field capacity). The spatial distribution of surface maize canopy temperature in the study area was extracted by the red green ratio index (RGRI) with the visible and thermal infrared images from UAVs. Specifically, the UAV images were selected as 5 days in 2020 and 7 days in 2021. The cumulative frequency was then analyzed for the canopy temperature on each thermal infrared image. Soil water content data at a depth of 40cm was also collected at the same time. Subsequently, two methods were proposed to improve the performance of CWSI. The accuracy was evaluated using the correlation between the CWSI and crop physiological indexes (Stomatal conductance, Gs; Net photosynthetic rate, Pn; and Transpiration rate, Tr) with the soil moisture content. Method 1, the canopy temperature was segmented using different statistical quantiles in the normal distribution. Some unreasonable values were then eliminated for the low and high temperature in the histogram of canopy temperature frequency. The commonly-used statistical quantiles were 93.32%, 84.13%, 69.15%, 61.79%, 50%, 38.21%, 30.85%, and 15.87%, respectively. The intervals 0.62% and 2.28% were used to eliminate the high temperature values on the right side of the frequency histogram, and then the CWSI (denoted as CWSITcF%) was calculated from the average canopy temperature over different statistical quantiles. The statistical quantiles with the best correlation with the crop physiological indexes were selected as the most sensitive canopy temperature interval. In Method 2, maize canopy data was divided into four parts: Interval Ⅰ, Tc <40, Var<10; Interval Ⅱ, Tc <40, 10< Var<20; Interval Ⅲ, 35< Tc <45, Var >20; and Interval Ⅳ, 40< Tc<50, 0< Var<20, according to the variance of canopy temperature. The most sensitive statistical quantile on the respective interval was then selected to calculate the CWSI (denoted as CWSIn). The results showed that: 1) The coefficients R2 of CWSIn and crop physiological indexes (Gs, Pn, Tr) calculated from 2020 and 2021 were 0.72, 0.52 and 0.62, and normalized root mean square error (nRMSE) were 23.96%, 24.06% and 25.60%. The accuracy of model fitting was higher than that of original CWSI (R2 were 0.73, 0.34, 0.46, nRMSE were 23.69%, 28.27%, 30.21%), but little different from CWSITcF% (R2 were 0.74, 0.54, 0.61, nRMSE were 22.87%, 23.74% and 25.61%) 2) The most sensitive canopy temperature range varied greatly between years (2020, 61.17%; 2021, 49.38%; and Two-year data, 83.51%), although the CWSITcF% was provided a higher accuracy of crop water stress. There was also some influence on the application of CWSITcF% in practice. The CWSIn presented a higher stability for the crop physiological indexes (The nRMSE between physiological indicators were 16.60%, 27.37% and 28.49% in 2020 and 21.60%, 18.95%, 22.64% in 2021). Therefore, the improved CWSIn can be expected to more accurately monitor the crop water stress under UAVs remote sensing, compared with the original.