Improved estimation of canopy water status in maize using UAV-based digital and hyperspectral images

Abstract

The estimation of water status of maize is important for evaluating crop growth and conducting precision irrigation. The development of unmanned aerial vehicles (UAVs) equipped with sensor technologies provides high-quality data for estimating maize water status. Only a few studies have been conducted on the estimation of maize equivalent water thickness (EWT) and fuel moisture content (FMC) using UAV hyperspectral images. This study aimed to estimate the leaf and canopy water status of maize inbred lines using UAV digital and hyperspectral data. Leaf area index (LAI) is required to obtain canopy water indicators, canopy equivalent water thickness (EWTc), and canopy fuel moisture content (FMCc). However, obtaining the LAI from remote sensing images requires the support of samples or prior knowledge. The LAI is positively correlated with canopy coverage (CC), which can be extracted accurately from UAV images. Therefore, for EWTc and FMCc, this study aimed to use the CC instead of the LAI to construct and test new canopy water indicators in order to improve the convenience of UAV imaging technology in monitoring maize water status. The results showed that, after the introduction of CC, two indicators, revised canopy equivalent water thickness (r-EWTc) and revised canopy fuel moisture content (r-FMCc), were both sensitive to the difference vegetation index (DVI) derived from UAV hyperspectral images. The r-FMCc was the most effective of the six water indicators used in this study, and the Pearson’s correlation coefficient (r) with DVI was 0.93. The results indicate that the CC extracted directly from UAV digital images is suitable to replace LAI, and helps improve the data availability for estimating canopy water status. The UAV hyperspectrum can accurately estimate the water status in maize inbred lines, which is helpful for further application of UAV data in breeding.