Evaluating the sensitivity of water stressed maize chlorophyll and structure based on UAV derived vegetation indices

Abstract To further assess the sensitivity of crop chlorophyll and structure based on UAV vegetation indices (VIs) to maize water stress, a study was carried out in a maize field located in Inner Mongolia, China, with various levels of deficit irrigation over the entire 2018 and 2019 growing seasons. Ground measurements of stomatal conductance (Gs), leaf area index and leaf chlorophyll were used as references for maize water status, canopy structure and chlorophyll content, respectively. Four structure VIs and two chlorophyll VIs, and three regression algorithms (multiple linear, random forest and artificial neural networks regression) were adopted. The results showed that canopy structure derived from VIs had a significant correlation (p