Abstract
Spectral remote sensing offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time. In contrast, the traditional methods for irrigation management are generally time-consuming, and numerous observations are required to characterize them. The aim of this study was to investigate the suitability of hyperspectral reflectance measurements of remote sensing technique for salinity and water stress condition. For this, the spectral indices of 5 maize cultivars were tested to assess canopy water content (CWC), canopy water mass (CWM), biomass fresh weight (BFW), biomass dry weight (BDW), cob yield (CY), and grain yield (GY) under full irrigation, full irrigation with salinity levels, and the interaction between full irrigation with salinity levels and water stress treatments. The results showed that the 3 water spectral indices (R970 − R900)/(R970 + R900), (R970 − R880)/(R970 + R880), and (R970 − R920)/(R970 + R920) showed close and highly significant associations with the mentioned measured parameters, and coefficients of determination reached up to R2 = 0.73*** in 2013. The model of spectral reflectance index (R970 − R900)/(R970 + R900) of the hyperspectral passive reflectance sensor presented good performance to predict the CY, GY, and CWC compared to CWM, BFW, and BDW under full irrigation with salinity levels and the interaction between full irrigation with salinity levels and water stress treatments. In conclusion, the use of spectral remote sensing may open an avenue in irrigation management for fast, high-throughput assessments of water status, biomass, and yield of maize cultivars under salinity and water stress conditions.
Highlights
Over the past few years, remote sensing techniques have been used as very useful tools to precisely monitor crops throughout their growing period to support decisions for good agricultural practices by taking advantages of numerous available technologies, such as electromagnetic induction, geographic positioning system, aerial imagery, thermography, reflectance sensing, and laser-induced chlorophyll fluorescence sensing (Mistele and Schmidhalter 2008; Thoren and Schmidhalter 2009; Elsayed et al 2015a)
The results showed that the 3 water spectral indices (R970 − R900)/(R970 + R900), (R970 − R880)/(R970 + R880), and (R970 − R920)/(R970 + R920) showed close and highly significant associations with the mentioned measured parameters, and coefficients of determination reached up to R2 = 0.73*** in 2013
The purpose of this study was to evaluate the performance of passive sensor to: (i) assess whether spectral indices can reflect changes in water status, biomass, and grain yield (GY) of maize cultivars under salinity and water stress conditions; (ii) build the model for predicting canopy water content (CWC), canopy water mass (CWM), biomass dry weight (BDW), biomass fresh weight (BFW), and GY based on the information data from the spectral water index (R970 − R900)/(R970 + R900); and (iii) study the effect of full irrigation without salinity, full irrigation with salinity levels, and interaction between full irrigation with salinity levels and water stress treatments on measured parameters
Summary
Over the past few years, remote sensing techniques have been used as very useful tools to precisely monitor crops throughout their growing period to support decisions for good agricultural practices by taking advantages of numerous available technologies, such as electromagnetic induction, geographic positioning system, aerial imagery, thermography, reflectance sensing, and laser-induced chlorophyll fluorescence sensing (Mistele and Schmidhalter 2008; Thoren and Schmidhalter 2009; Elsayed et al 2015a). The purpose of this study was to evaluate the performance of passive sensor to: (i) assess whether spectral indices can reflect changes in water status, biomass, and GY of maize cultivars under salinity and water stress conditions; (ii) build the model for predicting canopy water content (CWC), CWM, BDW, BFW, and GY based on the information data from the spectral water index (R970 − R900)/(R970 + R900); and (iii) study the effect of full irrigation without salinity, full irrigation with salinity levels, and interaction between full irrigation with salinity levels and water stress treatments on measured parameters
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