Remote estimation of grain yield based on UAV data in different rice cultivars under contrasting climatic zone

Abstract

Timely and accurate estimation of grain yield is valuable for crop monitoring and breeding, and plays an important role in precision agriculture. In this study, we developed a method to predict grain yield based entirely on unmanned aerial vehicle (UAV) data in different rice cultivars under two contrasting climatic regions. Vegetation indices (VIs), which were derived from canopy reflectance collected by UAV, were used to correlate with rice phenotyping and estimate grain yield. It is found that the two-band enhanced vegetation index (EVI2) closely related to leaf area index (LAI) as well as canopy chlorophyll content (CCC), and the red edge chlorophyll index (CIrededge) related to above ground biomass (AGB). Thus, the phenotyping-related VIs – EVI2 and CIrededge were exploited to develop yield estimation model. Results showed that the single stage VIs weakly correlated with grain yield of different rice cultivars and was not able to estimate grain yield. By contrast, the multi-temporal VIs can be used to estimate grain yield in different rice cultivars with the estimation error below 7.1 %. In addition, the rice growth duration differed in different climatic zones, which may decrease the estimation accuracy of grain yield by using multi-temporal VIs. After adjusting the phenological stage of multi-temporal VIs used in estimation model, the estimation accuracy of grain yield in different climatic zones increased. In conclusion, this study demonstrated that the UAV-based multi-temporal VIs were reliable for grain yield estimation in different rice cultivars under contrasting climatic zones.