Ground Hyper-Spectral Remote-Sensing Monitoring of Wheat Water Stress during Different Growing Stages

Monitoring agricultural drought via ground hyper-spectral remote sensing has always been a hot topic in the fields of agriculture and meteorology. In this study, a greenhouse experiment was conducted on wheat subjected to water stress during its different growth stages, namely tillering, jointing, and milk maturity. An instrument (HOBO ware PRO) used to continuously measure soil moisture was employed to measure the soil water content (SWC). An analytical spectral device (ASD) was utilized to obtain the spectral curve of wheat subject to different water treatment methods. The canopy temperature was obtained using thermal infrared sensors (METER SI-400). The relationships between the SWC, wheat drought stage, canopy temperature, and spectral response characteristics were elucidated. The results showed that the significant differences in spectral characteristics were due to water stress during the different growth stages of wheat. Red-edge parameters of red-valley position (RVP) and red-edge position (REP) both changed by 21 nm for the tillering-stage drought and the jointing-stage drought; however, the RVP and REP values for the milk maturity stage drought and the treatment under no water stress changed by 2 nm. Further, it was proved that the red-edge blue-shift phenomenon was affected not only by the different wheat growth processes, but also by the water stress at different growth stages. Red-edge reflectance clearly reflects wheat water stress at different growth stages. From SWC and canopy temperature analysis results, SWC and canopy temperature had a significant difference between wheat drought at different growth stages, and the canopy temperature at the jointing stage drought had the strongest change. The water index (WI) based on eleven vegetation water indexes exhibited a good performance for distinguishing wheat water stress at different growth stages. In conclusion, ground-based hyperspectral remote sensing can provide a large amount of high temporal and spectral resolution data on vegetation and its surrounding environment, making it an important technical tool for wheat drought monitoring, which has a great significance on the monitoring and early warning of wheat drought, reducing drought-related yield losses, and ensuring food security.